A method and system for intelligent interactive control of water production equipment

Abstract

The present invention relates to the field of intelligent control technology, and in particular to a method and system for intelligent interactive control of water production equipment. The method comprises the following steps: real-time monitoring of water production process data and synchronous processing of time gap filling for the water production process of the water production equipment throughout its life cycle to obtain pH data, particle concentration data, water flow data and temperature data of the water production process; water turbidity impact correction calculation of the particle concentration data of the water production process, and visualization drawing of data distribution and division of water production mode according to change trend in combination with water flow data and temperature data of the water production process, and water production process problem analysis and interactive control response of water production process problem points are performed to generate interactive control strategies for water production equipment water production process problem points to perform corresponding interactive control adjustment work for water production process problems. The present invention can improve the performance of water production equipment and user experience.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a method and system for intelligent interactive control of water production equipment. Background Art

[0002] In modern industrial and household applications, water production equipment plays an important role, such as water treatment (including reverse osmosis, distillation, ion exchange, ultrafiltration, nanofiltration, ultraviolet disinfection, etc.), purification and softening. By integrating a variety of intelligent sensors in water production equipment, water quality parameters (such as pH value, turbidity, dissolved solids, etc.) and equipment status (such as water flow, pressure, etc.) are monitored in real time. The sensor data is transmitted to the central control unit through the data acquisition module to provide basic data for intelligent control. By applying dynamic control algorithms, the working mode and parameters of the equipment are automatically adjusted according to real-time water quality data and user needs. The algorithm includes adaptive adjustment functions, which can optimize the water treatment process, improve water quality stability and treatment efficiency. At the same time, it also supports multiple operation modes such as touch screen, mobile application or voice control through the design of a friendly user interaction interface. Users can monitor the status of the equipment in real time, view water quality data, adjust settings and obtain maintenance reminders through the interface, thereby improving the convenience and personalized experience of operation. However, traditional water production equipment control methods are usually based on simple timers or manual adjustments, lack intelligent control and feedback mechanisms, and cannot automatically adjust working parameters according to real-time water quality data. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for intelligent interactive control of water production equipment to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for intelligent interactive control of water production equipment includes the following steps:

[0005] Step S1: real-time monitoring of the water production process data of the water production equipment during its entire life cycle is performed through the built-in pH monitoring equipment, particle concentration monitoring equipment, flow sensor and temperature sensor of the water production equipment to obtain real-time pH data of the water production process, real-time particle concentration data of the water production process, real-time water flow data of the water production process and real-time temperature data of the water production process; the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process are transmitted to the intelligent interactive control platform through a wireless transmission network, wherein the intelligent interactive control platform includes a data processing unit, an interactive user interface and an intelligent control unit;

[0006] Step S2: The real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process, and the real-time temperature data of the water production process are synchronously processed by the data processing unit in the intelligent interactive control platform to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process, and the temperature data of the water production process in the same time series dimension; based on the pH data of the water production process and the temperature data of the water production process, the particle concentration data of the water production process is corrected and calculated for the water quality turbidity, to obtain the water quality turbidity data of the water production process;

[0007] Step S3: Visualize the data distribution of water quality turbidity data, water flow data and temperature data of the water production process through the interactive user interface in the intelligent interactive control platform to generate water quality turbidity change curves, water flow change curves and temperature change curves; divide the water quality turbidity change curves, water flow change curves and temperature change curves into water production modes according to change trends to obtain the water quality parameter change conditions under different water production process working mode stages, wherein the different water production process working mode stages include reverse osmosis water production mode, distillation water production mode, ion exchange water production mode, ultrafiltration water production mode, nanofiltration water production mode and ultraviolet disinfection water production mode;

[0008] Step S4: Obtain the operating status of the water production equipment in different water production process working mode stages through the interactive user interface, and perform water production process problem analysis on the corresponding water production process working mode stages based on the operating status of the water production equipment in different water production process working mode stages and the change status of water quality parameters, and obtain the water production process working mode problem points of the water production equipment; perform water production process problem point interactive control response to the water production process working mode problem points of the water production equipment through the intelligent control unit in the intelligent interactive control platform and the built-in execution unit of the water production equipment, generate an interactive control strategy for the water production process problem points of the water production equipment, and execute the corresponding water production process problem interactive control adjustment work.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: Real-time monitoring of pH data of the water production process of the water production equipment throughout its life cycle is performed using a pH monitoring device built into the water production equipment to obtain real-time pH data of the water production process;

[0011] Step S12: using a particle concentration monitoring device built into the water production equipment to monitor particle concentration data in real time during the entire life cycle of the water production equipment, so as to obtain real-time particle concentration data of the water production process;

[0012] Step S13: using a built-in flow sensor of the water production equipment to monitor the water flow data of the water production equipment in real time during the entire life cycle of the water production equipment, so as to obtain real-time water flow data of the water production process;

[0013] Step S14: using a temperature sensor built into the water making equipment to monitor the temperature data of the water making process of the water making equipment in real time throughout its life cycle, so as to obtain real-time temperature data of the water making process;

[0014] Step S15: The real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process are transmitted to the intelligent interactive control platform through the wireless transmission network, wherein the intelligent interactive control platform includes a data processing unit, an interactive user interface and an intelligent control unit.

[0015] Further, step S2 includes the following steps:

[0016] Step S21: performing noise point elimination processing on the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process, and the real-time temperature data of the water production process through the data processing unit in the intelligent interactive control platform, so as to obtain pH denoised data of the water production process, particle concentration denoised data of the water production process, water flow denoised data of the water production process, and temperature denoised data of the water production process;

[0017] Step S22: performing histogram abnormal distribution cleaning on the pH denoised data of the water production process, the particle concentration denoised data of the water production process, the water flow denoised data of the water production process, and the temperature denoised data of the water production process to obtain pH abnormality cleaning data of the water production process, particle concentration abnormality cleaning data of the water production process, water flow abnormality cleaning data of the water production process, and temperature abnormality cleaning data of the water production process;

[0018] Step S23: standardizing the water production process pH abnormality cleaning data, the water production process particle concentration abnormality cleaning data, the water production process water flow abnormality cleaning data, and the water production process temperature abnormality cleaning data to obtain water production process pH standard data, water production process particle concentration standard data, water production process water flow standard data, and water production process temperature standard data;

[0019] Step S24: performing time series gap filling and synchronization processing on the pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process, and the temperature standard data of the water production process, so as to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process, and the temperature data of the water production process in the same time series dimension;

[0020] Step S25: Based on the pH data and the temperature data of the water production process, the particle concentration data of the water production process is corrected for the water turbidity effect to obtain the water turbidity data of the water production process.

[0021] Further, step S24 includes the following steps:

[0022] Step S241: dividing the pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process, and the temperature standard data of the water production process into a set of time series point windows to obtain a subset of the water production process monitoring parameter data at each time series point;

[0023] Step S242: performing data missing identification analysis on a subset of water production process monitoring parameter data at each time sequence point to obtain missing sub-data of the water production process at each time sequence point;

[0024] Step S243: performing arithmetic mean calculation of the missing point gap of the missing sub-data of the water production process at each time sequence point to obtain the missing point gap average value of the water production process at each time sequence point;

[0025] Step S244: Based on the average value of the gap between missing points in the water production process at each time point, the corresponding missing sub-data in the pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process and the temperature standard data of the water production process are synchronously processed to fill the time gaps, so as to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process and the temperature data of the water production process in the same time dimension.

[0026] Further, step S25 includes the following steps:

[0027] Step S251: performing particle concentration spatial distribution analysis on the particle concentration data of the water production process to obtain a particle concentration spatial distribution map of the water production process;

[0028] Step S252: Perform particle spatial distribution area planning analysis on the particle concentration spatial distribution diagram of the water production process to obtain the size of the particle concentration distribution area of ​​the water production process;

[0029] Step S253: performing an initial quantitative calculation of regional water quality turbidity on the particle concentration data of the water production process based on the size of the particle concentration distribution area of ​​the water production process, so as to obtain initial turbidity data of regional water quality of the water production process;

[0030] Step S254: Based on the pH data of the water production process and the temperature data of the water production process, the particle concentration spatial distribution diagram of the water production process is evaluated and analyzed to obtain the pH change-particle distribution influence factor of the water production process and the temperature change-particle distribution influence factor of the water production process;

[0031] Step S255: Based on the pH change-particle distribution influencing factor of the water production process and the temperature change-particle distribution influencing factor of the water production process, the water quality turbidity impact correction calculation is performed on the initial turbidity data of the water quality in the water production process area to obtain the water quality turbidity data of the water production process.

[0032] Further, step S254 includes the following steps:

[0033] Perform frequency change multi-scale characteristic analysis on pH data of water production process and temperature data of water production process to obtain multi-scale characteristic data of frequency change of pH of water production process and multi-scale characteristic data of frequency change of temperature of water production process;

[0034] Based on the multi-scale characteristic data of pH frequency changes in the water production process and the multi-scale characteristic data of temperature frequency changes in the water production process, the local variability evaluation and analysis of the spatial distribution map of particle concentration in the water production process was carried out to obtain the spatial variability data of pH frequency change-particle concentration local distribution and the spatial variability data of temperature frequency change-particle concentration local distribution;

[0035] Perform spatial correlation analysis on the pH frequency change-particle concentration local distribution spatial variability data and the temperature frequency change-particle concentration local distribution spatial variability data to obtain the spatial correlation relationship between the pH frequency change and the particle concentration distribution position and the spatial correlation relationship between the temperature frequency change and the particle concentration distribution position;

[0036] Based on the spatial correlation between pH frequency change and particle concentration distribution position, the dynamic impact of pH change on the particle concentration spatial distribution map of the water production process is evaluated and analyzed to obtain the pH change-particle distribution influencing factor of the water production process;

[0037] Based on the spatial correlation between temperature frequency change and particle concentration distribution position, the dynamic impact of temperature change on the spatial distribution diagram of particle concentration in the water production process is evaluated and analyzed to obtain the temperature change-particle distribution influencing factor of the water production process.

[0038] Further, step S3 includes the following steps:

[0039] Step S31: Visualizing the data distribution of water quality turbidity data, water flow data and temperature data of the water production process through an interactive user interface in the intelligent interactive control platform to generate a water quality turbidity change curve, a water flow change curve and a temperature change curve;

[0040] Step S32: performing a mapping conversion of the water quality turbidity change curve, the water flow change curve and the temperature change curve in the same coordinate system to obtain a water quality turbidity curve, a water flow curve and a temperature curve in the same time series coordinate system;

[0041] Step S33: performing curve change trend recognition analysis on the water quality turbidity curve, water flow curve and temperature curve in the same time series coordinate system to obtain the water quality turbidity curve change trend, water flow curve change trend and temperature curve change trend in the same time series coordinate system;

[0042] Step S34: determining the water production mode time interval of the corresponding water quality turbidity curve, water flow curve and temperature curve based on the change trend of the water quality turbidity curve, the change trend of the water flow curve and the change trend of the temperature curve in the same time series coordinate system, so as to obtain the curve time series intervals of different water production process working mode stages, wherein the different water production process working mode stages include reverse osmosis water production mode, distillation water production mode, ion exchange water production mode, ultrafiltration water production mode, nanofiltration water production mode and ultraviolet disinfection water production mode;

[0043] Step S35: Based on the curve time series division intervals of different water production process working mode stages, the water quality turbidity curve change trend, water flow curve change trend and temperature curve change trend in the same time series coordinate system are divided into water production mode stages to obtain the water quality parameter change status under different water production process working mode stages.

[0044] Further, step S34 includes the following steps:

[0045] Based on the changing trends of the water quality turbidity curve, the water flow curve and the temperature curve in the same time series coordinate system, the changing trend identification and analysis of the water production process working mode corresponding to the water production equipment is carried out. When the changing trend of the water quality turbidity curve shows a continuous downward trend, the changing trend of the water flow curve shows a low upward trend and the changing trend of the temperature curve shows a fluctuation trend within the normal temperature range, the water production process working mode is judged to be the reverse osmosis water production mode; when the changing trend of the water quality turbidity curve shows a low and gentle downward trend, the changing trend of the water flow curve also shows a low and gentle upward trend and the changing trend of the temperature curve shows a continuous upward trend, the water production process working mode is judged to be the distillation water production mode; when the changing trend of the water quality turbidity curve shows a trend of small fluctuations, the changing trend of the water flow curve shows a continuous upward trend and the changing trend of the temperature curve shows a normal When the turbidity curve of water quality shows a significant downward trend, the water flow curve shows a continuous upward trend, and the temperature curve shows a fluctuation trend within the normal temperature range, the working mode of the water making process is determined to be the ultrafiltration water making mode; when the turbidity curve of water quality shows a median downward trend, the water flow curve shows a median slow upward trend, and the temperature curve shows a fluctuation trend within the normal temperature range, the working mode of the water making process is determined to be the nanofiltration water making mode; and when the turbidity curve of water quality shows a trend of small fluctuations, the water flow curve shows an irregular fluctuation upward trend, and the temperature curve shows a fluctuation trend within the normal temperature range, the working mode of the water making process is determined to be the ultraviolet disinfection water making mode;

[0046] The above-mentioned water production process working mode discrimination process is designed as the water production equipment working mode stage division standard, and based on the water production equipment working mode stage division standard, the water quality turbidity curve, water flow curve and temperature curve in the same time coordinate system are used to determine the water production mode timing interval to obtain the curve timing division intervals of different water production process working mode stages.

[0047] Further, step S4 includes the following steps:

[0048] Step S41: obtaining the operating status of the water production equipment in different water production process working mode stages through an interactive user interface;

[0049] Step S42: based on the operation status of the water production equipment and the change status of the water quality parameters in different water production process working mode stages, the water production process problem analysis is performed for the corresponding water production process working mode stage to obtain the problem points of the water production equipment water production process working mode;

[0050] Step S43: performing water production process problem point response processing on the water production process working mode problem point of the water production equipment through the intelligent control unit in the intelligent interactive control platform to generate a water production process problem point response control instruction for the water production equipment;

[0051] Step S44: Send a water production process problem point response control instruction to the built-in execution unit of the water production equipment through the intelligent control unit, and perform water production process problem point interactive control response to the water production process working mode problem point of the water production equipment, generate a water production process problem point interactive control strategy for the water production equipment, and execute corresponding water production process problem interactive control adjustment work.

[0052] Furthermore, the present invention also provides a system for intelligent interactive control of water production equipment, which is used to execute the method for intelligent interactive control of water production equipment as described above, and the system for intelligent interactive control of water production equipment includes:

[0053] The water quality parameter monitoring module of the water production process is used to monitor the water production process data of the water production equipment in real time during the whole life cycle of the water production process through the built-in pH monitoring equipment, particle concentration monitoring equipment, flow sensor and temperature sensor of the water production equipment, so as to obtain the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process; the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process are transmitted to the intelligent interactive control platform through the wireless transmission network, wherein the intelligent interactive control platform includes a data processing unit, an interactive user interface and an intelligent control unit;

[0054] The water quality parameter processing module of the water production process is used to perform time series gap filling and synchronous processing on the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process through the data processing unit in the intelligent interactive control platform, so as to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process and the temperature data of the water production process in the same time series dimension; based on the pH data of the water production process and the temperature data of the water production process, the particle concentration data of the water production process is corrected and calculated for the water quality turbidity, so as to obtain the water quality turbidity data of the water production process;

[0055] The module for visualizing and dividing the water production mode of the water production process is used to visualize the data distribution of the water quality turbidity data, the water flow data and the temperature data of the water production process through the interactive user interface in the intelligent interactive control platform, so as to generate the water quality turbidity change curve, the water flow change curve and the temperature change curve; the water quality turbidity change curve, the water flow change curve and the temperature change curve are divided into water production modes according to the change trend, so as to obtain the water quality parameter change status under different water production process working mode stages, wherein the different water production process working mode stages include reverse osmosis water production mode, distillation water production mode, ion exchange water production mode, ultrafiltration water production mode, nanofiltration water production mode and ultraviolet disinfection water production mode;

[0056] The water production process problem point interactive control module is used to obtain the operating status of the water production equipment under different water production process working mode stages through an interactive user interface, and analyze the water production process problems in the corresponding water production process working mode stages based on the operating status of the water production equipment under different water production process working mode stages and the change status of water quality parameters, and obtain the water production process working mode problem points of the water production equipment; through the intelligent control unit in the intelligent interactive control platform and the built-in execution unit of the water production equipment, the water production process problem point interactive control response is performed to the water production process working mode problem points of the water production equipment, and an interactive control strategy for the water production process problem points of the water production equipment is generated to execute the corresponding water production process problem interactive control adjustment work.

[0057] Beneficial effects of the present invention:

[0058] 1. Compared with the prior art, the method for intelligent interactive control of water-making equipment proposed in the present invention has the beneficial effect of real-time monitoring of pH data of the water-making process of the water-making equipment throughout its life cycle by using the built-in pH monitoring device of the water-making equipment. Continuous monitoring of pH value can help timely discover abnormal changes in water quality, thereby avoiding possible equipment damage and ensuring that the water-making process meets quality standards, and preventing the impact on the final product. By using the built-in particle concentration monitoring device of the water-making equipment to monitor the particle concentration data of the water-making process of the water-making equipment throughout its life cycle, it is helpful to identify changes in solid matter in the water, and high particle concentration indicates that the efficiency of the filtration system is reduced or the equipment is faulty, thereby affecting the water quality, which can provide basic data guarantee for the subsequent turbidity calculation process. By using the built-in flow sensor of the water-making equipment to monitor the water flow data of the water-making process of the water-making equipment throughout its life cycle, the smoothness of the water flow in the water-making process can be monitored in real time, and the flow sensor can provide accurate flow data, thereby helping to optimize the use of water resources. The temperature data of the water production process of the water production equipment throughout its life cycle is also monitored in real time by using the temperature sensor built into the water production equipment, which helps to identify whether the equipment is working within the normal temperature range. Too high or too low temperature will cause the water production equipment to malfunction or affect the water production effect, thereby providing data support for the subsequent turbidity correction calculation process. At the same time, by using a wireless transmission network to transmit real-time data to the intelligent interactive control platform, the efficiency of data processing and response is greatly improved. The intelligent interactive control platform can integrate and analyze these real-time data, provide an intuitive user interface and intelligent control functions, and users can monitor various indicators of the water production process in real time through the interactive interface, and optimize and adjust according to data feedback. The intelligent control unit can also automatically perform some adjustment operations, such as adjusting chemical dosage or changing filtration settings, to ensure the best performance of the water production process. Secondly, by using the data processing unit in the intelligent interactive control platform to perform time series gap filling and synchronous processing on the real-time pH data, real-time particle concentration data, real-time water flow data and real-time temperature data of the water production process, the continuity and consistency of the real-time monitoring data of the water production process in the time dimension can be ensured. In the actual water production process, data collection may have gaps or missing due to various reasons, which will affect the comprehensiveness and accuracy of data analysis. By filling the time series gaps of standardized data, the integrity of the data can be restored to ensure that the data is consistent at the same time point. This synchronous processing makes subsequent data analysis and model calculation more reliable because it eliminates errors caused by data gaps, thereby improving the accuracy of time series analysis, ensuring that all key data are compared and analyzed in the same time series dimension, and improving the grasp and prediction capabilities of dynamic changes in the water production process.By performing a water quality turbidity correction calculation on the particle concentration data of the water production process based on the pH data of the water production process and the temperature data of the water production process, the actual water quality condition can be evaluated more accurately. The pH value and temperature have a significant impact on the turbidity of water quality. By incorporating these factors into the calculation, the particle concentration data can be more comprehensively understood and corrected. This correction calculation can eliminate the errors caused by pH changes and temperature fluctuations, making the final water quality turbidity data more real and reliable, which is very important for evaluating the purification effect and water quality stability of the water production system, and helps to optimize water quality management measures, thereby improving the safety of the final water quality and the degree of compliance with standards. Then, by using the interactive user interface in the intelligent interactive control platform to visualize the data distribution of the water quality turbidity data, water flow data and temperature data of the water production process, the water quality turbidity, water flow and temperature data of the water production process can be effectively displayed, and the water quality turbidity change curve, water flow change curve and temperature change curve can be generated. It not only intuitively reflects the changes of these key parameters over time, but also helps operators quickly identify data anomalies or trends. This visualization method enables users to monitor the system operation status in real time, thereby providing basic data guarantee for subsequent water production mode analysis. By dividing the water quality turbidity change curve, water flow change curve and temperature change curve into water production mode periods according to the change trend, the changes in water quality parameters under each water production mode can be understood in detail. This stage division not only helps to analyze the performance of each mode, but also reveals the water quality fluctuation characteristics under different water production modes. Through this detailed stage analysis, users can formulate more scientific operation and adjustment plans, optimize each water production working mode stage in the water production process, so as to realize intelligent water production control and feedback mechanism, improve the stability of water quality and the operation efficiency of water production equipment. Finally, the operating status of the water-making equipment in different working modes of the water-making process is obtained by using an interactive user interface (UI). This real-time data collection capability is the basis for optimizing the water-making system. The UI interface allows operators to intuitively monitor the equipment status, such as key parameters such as flow, pressure, current, and temperature, so as to observe the performance of the equipment in different working modes. This real-time monitoring not only improves the visual management of the equipment operating status, but also enhances the early detection of potential equipment problems, allowing operators to better understand the behavior of the equipment in different working modes, providing a detailed data basis for subsequent analysis and optimization. By analyzing the water-making process problems in the corresponding working mode stages of the water-making process based on the operating status of the water-making equipment in different working mode stages of the water-making process and the changes in water quality parameters, it is helpful to have a deep understanding of the potential problems in the water-making process. The key to this stage is to identify the problem points in the water-making process through data analysis, such as low equipment operating efficiency, high energy consumption, and unstable water quality.Through detailed analysis of operating data and water quality parameters, the performance bottleneck of the equipment under a specific working mode and its impact on water quality can be revealed. This in-depth analysis can not only help find potential problems in the water production process, but also provide improvement suggestions. In addition, the intelligent control unit in the intelligent interactive control platform and the built-in execution unit of the water production equipment are used to interactively control the water production process problem points of the water production process of the water production equipment, which can effectively realize the automatic response processing of the water production process problem points. The intelligent control unit at this stage can automatically respond to generate corresponding control instructions according to the analysis results. This automatic response capability greatly improves the flexibility and response speed of the water production equipment, so that control instructions can be generated and sent in real time, ensuring that the water production equipment can make adjustments quickly when facing problems, so as to send response control instructions to the built-in execution unit of the water production equipment and realize interactive control response to the water production process problem points of the water production equipment. An efficient control interaction mechanism can be established between the intelligent control unit and the equipment execution unit, making the adjustment of the water production process problem points more accurate and timely. By sending control instructions to the execution unit and performing interactive control, the equipment parameters can be adjusted in real time or the working mode can be changed to deal with the identified water production process problems. This precise control strategy can optimize the operating status of the equipment, ensure that the water quality parameters are within the optimal range, and maintain the stability of the water production process. By formulating and implementing interactive control strategies, the working parameters of the water production equipment can be automatically adjusted according to the real-time water quality data, and the adaptability and processing capacity of the water production equipment can be improved. These measures help to maintain the long-term stable operation of the water production equipment and optimize the water production effect, thereby improving production efficiency and equipment life.

[0059] 2. The system of intelligent interactive control of water-making equipment proposed in the present invention is generally composed of a water quality parameter monitoring module for the water production process, a water quality parameter processing module for the water production process, a water production mode visualization division module for the water production process, and an interactive control module for problem points in the water production process. It can implement the method of any intelligent interactive control of water-making equipment described in the present invention, and is used to combine the operations between computer programs running on each module to implement the method of intelligent interactive control of water-making equipment. The internal structures of the system cooperate with each other, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient process of intelligent interactive control of water-making equipment, thereby simplifying the operating flow of the system of intelligent interactive control of water-making equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0061] Figure 1 A schematic flow chart of the steps of the method for intelligent interactive control of water production equipment according to the present invention;

[0062] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0063] Figure 3 for Figure 2 Detailed step flow chart of step S15 in FIG. DETAILED DESCRIPTION

[0064] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0065] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0066] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0067] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for intelligent interactive control of water production equipment. In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the steps of the method for intelligent interactive control of water production equipment of the present invention. In this example, the method for intelligent interactive control of water production equipment includes the following steps:

[0068] Step S1: real-time monitoring of the water production process data of the water production equipment during its entire life cycle is performed through the built-in pH monitoring equipment, particle concentration monitoring equipment, flow sensor and temperature sensor of the water production equipment to obtain real-time pH data of the water production process, real-time particle concentration data of the water production process, real-time water flow data of the water production process and real-time temperature data of the water production process; the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process are transmitted to the intelligent interactive control platform through a wireless transmission network, wherein the intelligent interactive control platform includes a data processing unit, an interactive user interface and an intelligent control unit;

[0069] In an embodiment of the present invention, the pH of the water production process of the water production equipment is monitored in real time by using the built-in pH monitoring device of the water production equipment, so as to measure the pH value of the water sample in real time through the electrode sensor. The electrode generates a voltage signal through an electrochemical reaction to reflect the acidity and alkalinity of the water sample. The signal is amplified and filtered internally and then processed by an analog-to-digital converter (ADC) to ensure the accuracy and stability of the data. The monitoring device obtains the pH value of the water sample every second, thereby monitoring and obtaining real-time pH data of the water production process. The particle concentration of the water production process of the water production equipment is monitored in real time by using the built-in particle concentration monitoring device of the water production equipment, so as to monitor the particulate matter in the water in real time through laser scattering or optical sensors. When the water flows through the sensor, the device irradiates the water with a laser and measures the intensity of the scattered light to evaluate the concentration of the particles, thereby monitoring and obtaining real-time particle concentration data of the water production process. By using the built-in flow sensor of the water-making equipment to monitor the water flow rate in real time during the whole life cycle of the water-making equipment, the turbine flowmeter or electromagnetic flowmeter technology is used to monitor the water flow rate in real time. The turbine flowmeter uses the rotation speed of the rotating turbine blades to calculate the water flow rate, while the electromagnetic flowmeter determines the flow rate by measuring the electromotive force generated by the water flow through the magnetic field, thereby monitoring the real-time water flow data of the water-making process. At the same time, by using the built-in temperature sensor of the water-making equipment to monitor the temperature of the whole life cycle of the water-making equipment in real time, the water temperature is monitored in real time through thermocouple or thermistor technology. Thermocouples use the voltage change between two different metals to measure the temperature, while thermistors detect the temperature through the change of resistance, thereby monitoring the real-time temperature data of the water-making process. Then, the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process, and the real-time temperature data of the water production process obtained by previous monitoring are transmitted to a pre-built intelligent interactive control platform through a wireless transmission network according to the set time interval or trigger condition. The intelligent interactive control platform integrates a data processing unit, an interactive user interface and an intelligent control unit, wherein the data processing unit processes, analyzes and stores the received real-time data to generate detailed reports and charts; the interactive user interface allows the user to view and operate the data reports and charts in real time, and view the corresponding equipment working mode parameters; the intelligent control unit automatically adjusts the equipment operating status based on the data analysis results to achieve optimal water production control.

[0070] Step S2: The real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process, and the real-time temperature data of the water production process are synchronously processed by the data processing unit in the intelligent interactive control platform to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process, and the temperature data of the water production process in the same time series dimension; based on the pH data of the water production process and the temperature data of the water production process, the particle concentration data of the water production process is corrected and calculated for the water quality turbidity, to obtain the water quality turbidity data of the water production process;

[0071] In an embodiment of the present invention, a noise point detection algorithm is used by a data processing unit in an intelligent interactive control platform to eliminate noise points of previously received real-time pH data of a water production process, real-time particle concentration data of a water production process, real-time water flow data of a water production process, and real-time temperature data of a water production process. A noise point detection method based on a median filter is used to identify and eliminate abnormal fluctuations. The median filter can effectively remove isolated noise points in the data. The abnormal distribution of the histogram of the water production process data previously obtained after denoising is cleaned to generate a histogram using a statistical analysis tool, and outlier distribution points in the distribution are identified. The outliers are eliminated according to their frequency distribution and standard deviation to ensure the uniformity of the data set. The Z-score standardization method is also used to subtract the mean of the data set from each data point in the water production process data obtained after abnormal cleaning, and then divided by the standard deviation, so that the data is converted into a standard normal distribution with a mean of 0 and a standard deviation of 1. This process uses the standardization formula: ,in is the original data point, is the mean, is the standard deviation. At the same time, the standard data of the water production process obtained after standardization are synchronously processed by filling the time series gaps, so as to apply interpolation algorithms, such as linear interpolation or spline interpolation, to fill the missing data points in the time series gaps, so as to ensure that all the water production process data are aligned in the same time dimension. In the specific operation, each data series is interpolated, and the continuity of the time series is maintained during the insertion of missing values, so as to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process and the temperature data of the water production process in the same time series dimension. Then, the water quality turbidity impact correction calculation is performed on the corresponding water production process particle concentration data by combining the water production process pH data and water production process temperature data obtained after time series synchronization. The linear regression model is applied to perform regression analysis with pH data and temperature data as independent variables and particle concentration data as dependent variables. The particle concentration data is adjusted through the coefficients of the regression model, that is, the corresponding pH change influencing factors and temperature change influencing factors to eliminate the effects of pH and temperature on particle concentration, thereby obtaining the corrected water quality turbidity data, which reflects the actual water quality after excluding pH and temperature factors, and finally the water quality turbidity data of the water production process is obtained.

[0072] Step S3: Visualize the data distribution of water quality turbidity data, water flow data and temperature data of the water production process through the interactive user interface in the intelligent interactive control platform to generate water quality turbidity change curves, water flow change curves and temperature change curves; divide the water quality turbidity change curves, water flow change curves and temperature change curves into water production modes according to change trends to obtain the water quality parameter change conditions under different water production process working mode stages, wherein the different water production process working mode stages include reverse osmosis water production mode, distillation water production mode, ion exchange water production mode, ultrafiltration water production mode, nanofiltration water production mode and ultraviolet disinfection water production mode;

[0073] In an embodiment of the present invention, water turbidity, water flow rate and temperature data in a water production process are received by using an interactive user interface in an intelligent interactive control platform, and these data are plotted into a graph by using a data visualization tool in the interactive user interface (such as the Matplotlib library in Python or commercial data visualization software such as Tableau). Changes in water turbidity data should be plotted as a water turbidity change curve, water flow data should be plotted as a water flow change curve, and temperature data should be plotted as a temperature change curve. These curves should be able to accurately reflect the trend of data changes over time and mark important time nodes, thereby generating corresponding water turbidity change curves, water flow change curves and temperature change curves.At the same time, the time series data processing technology is used to map the coordinate system of the drawn water quality turbidity change curve, water flow change curve and temperature change curve to map them to the same coordinate system to ensure that the data points of all curves have the same timestamp or perform time alignment processing. The data alignment algorithm (such as interpolation or resampling) is also used to convert the curve to a unified time axis, and the water quality turbidity curve, water flow curve and temperature curve are analyzed for change trends in the same time series coordinate system to fit each curve using trend analysis tools (such as the curve fitting function in the SciPy library in Python) to identify water production processes. The trend line of the process monitoring data is obtained by calculating the derivative of each curve to identify the rate of increase or decrease, and using sliding average or other smoothing techniques to reduce the impact of noise. By analyzing these trend lines, the change trend of each curve is obtained, including the growth, decline or stable stage. Then, by combining the curve change trend obtained by the previous identification analysis, the corresponding water quality turbidity curve, water flow curve and temperature curve are identified and determined for the time period of different water production modes, so as to identify the typical time period of each water production mode by marking the characteristic points of trend changes. For example, in the reverse osmosis water production mode, the turbidity continues to decrease, the water flow rate rises from a low level, and the temperature is around normal temperature. In the distillation water-making mode, the turbidity drops slowly from a low level, the water flow rate rises slowly from a low level, and the temperature continues to rise; in the ion exchange water-making mode, the turbidity fluctuates slightly, the water flow rate continues to rise, and the temperature fluctuates within the normal temperature range; in the ultrafiltration water-making mode, the turbidity drops significantly, the water flow rate continues to rise, and the temperature fluctuates within the normal temperature range; in the nanofiltration water-making mode, the turbidity drops in the median, the water flow rate rises slowly in the median, and the temperature fluctuates within the normal temperature range; in the ultraviolet disinfection water-making mode, the turbidity fluctuates slightly, the water flow rate rises irregularly, and the temperature fluctuates within the normal temperature range. Using these trend characteristics, the entire time series can be The data is divided into multiple intervals, each interval corresponds to a water production mode, and the corresponding water turbidity curve change trend, water flow curve change trend and temperature curve change trend are divided into detailed water production mode stages by combining the intervals of different water production process working mode stages obtained previously. In this process, data segmentation and interval division techniques (such as K-means clustering algorithm) are required to divide the data into multiple stages. Each stage corresponds to a specific water production mode, and the water quality parameter changes under each water production mode are determined, including the fluctuation range of water turbidity, the change trend of water flow and the temperature fluctuation. Finally, the water quality parameter changes under different water production process working mode stages are obtained.

[0074] Step S4: Obtain the operating status of the water production equipment in different water production process working mode stages through the interactive user interface, and perform water production process problem analysis on the corresponding water production process working mode stages based on the operating status of the water production equipment in different water production process working mode stages and the change status of water quality parameters, and obtain the water production process working mode problem points of the water production equipment; perform water production process problem point interactive control response to the water production process working mode problem points of the water production equipment through the intelligent control unit in the intelligent interactive control platform and the built-in execution unit of the water production equipment, generate an interactive control strategy for the water production process problem points of the water production equipment, and execute the corresponding water production process problem interactive control adjustment work.

[0075] In an embodiment of the present invention, an interactive user interface (UI) is used to monitor in real time the equipment operation status of the water production equipment in different working mode stages (including water production working modes such as reverse osmosis, distillation, ion exchange, ultrafiltration, nanofiltration, and ultraviolet disinfection). The UI interface graphically presents the equipment parameters and status in each working mode. For example, in the reverse osmosis mode, the interface displays information such as the pump flow rate, pressure sensor reading, and membrane flux; in the distillation mode, the interface displays data such as the temperature of the heater and the cooling effect of the condenser. These data are obtained through embedded sensors and data acquisition modules, and are transmitted to the interactive user interface in the intelligent interactive control platform through industrial communication protocols (such as Modbus and Profibus), thereby obtaining the operation status of the water production equipment in different working mode stages of the water production process. At the same time, by combining the operating conditions of the water-making equipment under different working mode stages of the water-making process obtained in the previous analysis and the changes in water quality parameters (such as changes in turbidity, water flow, temperature, etc.), the corresponding working mode stages of the water-making process are identified and analyzed. By using data analysis tools such as Pandas and NumPy libraries in Python, the operating data of the water-making equipment under different working modes are processed and analyzed, and the changing trends of water quality parameters over time are displayed by constructing mathematical models and data visualization tools (such as Matplotlib or Seaborn), thereby identifying the working mode stage where problems occur. For example, if an abnormal increase in water quality parameters is observed in the ion exchange mode, it may indicate saturation or damage of the ion exchange resin. The corresponding analysis results are summarized to generate a report, and each problem point identified by the water-making equipment under different working modes is recorded in detail, thereby obtaining the problem points of the water-making process working mode of the water-making equipment.Then, the previously identified water production equipment problem points are responded to and processed by using the intelligent control unit (such as PLC or DCS system) in the intelligent interactive control platform. The intelligent control unit generates control instructions according to the preset control strategy and the specific situation of the problem point. For example, if it is found that the pressure of the reverse osmosis membrane exceeds the normal range, the control unit will generate instructions to adjust the water inlet flow rate or start the membrane cleaning program. The generated control instructions include specific operation steps and parameter settings, and the generated control instructions are sent to the built-in execution unit of the water production equipment (such as electric valves, pump speed control devices, heater control systems, etc.) by using the intelligent control unit. The execution unit receives and executes these instructions. For example, when receiving the instruction to adjust the reverse osmosis membrane pressure, the electric valve will adjust the water inlet flow rate, and the pump speed control device will change the speed. The execution unit performs interactive control response through a feedback mechanism (such as a sensor monitoring real-time pressure changes) to ensure the effectiveness of the adjustment measures. The interactive control strategy for the water production process problem points of the water production equipment generated in the end will optimize and adjust the water production equipment, and feedback the adjustment results on the UI interface to ensure that the water production equipment can maintain a stable operating state in the next operation, thereby performing the corresponding interactive control adjustment work for the water production process problem.

[0076] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0077] Step S11: Real-time monitoring of pH data of the water production process of the water production equipment throughout its life cycle is performed using a pH monitoring device built into the water production equipment to obtain real-time pH data of the water production process;

[0078] In an embodiment of the present invention, the pH of the water production process of the water production equipment throughout its life cycle is monitored in real time by using a pH monitoring device built into the water production equipment, so that the pH value of the water sample is measured in real time through an electrode sensor. The electrode generates a voltage signal through an electrochemical reaction to reflect the acidity and alkalinity of the water sample. The signal is internally amplified and filtered, and then processed by an analog-to-digital converter (ADC) to ensure the accuracy and stability of the data. The monitoring device obtains the pH value of the water sample every second, and stores the data in a local data cache through an embedded controller, and finally monitors and obtains real-time pH data of the water production process.

[0079] Step S12: using a particle concentration monitoring device built into the water production equipment to monitor particle concentration data in real time during the entire life cycle of the water production equipment, so as to obtain real-time particle concentration data of the water production process;

[0080] In an embodiment of the present invention, the particle concentration is monitored in real time during the entire life cycle of the water production process of the water production equipment by using a particle concentration monitoring device built into the water production equipment, so as to monitor particulate matter in the water in real time through laser scattering or optical sensors. When water flows through the sensor, the device evaluates the concentration of particles by irradiating the laser and measuring the intensity of scattered light. The sensor makes an accurate assessment of the size and number of particles in the water flow, and converts the detected particle concentration data into electrical signals. The internal signal processing module performs data processing and calibration, and the real-time data is stored in the device memory. Finally, the real-time particle concentration data of the water production process is monitored.

[0081] Step S13: using a built-in flow sensor of the water production equipment to monitor the water flow data of the water production equipment in real time during the entire life cycle of the water production equipment, so as to obtain real-time water flow data of the water production process;

[0082] In an embodiment of the present invention, the water flow rate is monitored in real time during the entire life cycle of the water production process of the water production equipment by using a flow sensor built into the water production equipment, and the water flow rate is monitored in real time by using turbine flowmeter or electromagnetic flowmeter technology. The turbine flowmeter calculates the water flow rate by the rotation speed of the rotating turbine blades, and the electromagnetic flowmeter determines the flow rate by measuring the electromotive force generated by the water flow through the magnetic field. The sensor converts the flow data into an electrical signal, which is processed and calibrated by a data acquisition module, and the data is periodically sent to a local data storage device, so as to finally monitor and obtain real-time water flow data of the water production process.

[0083] Step S14: using a temperature sensor built into the water making equipment to monitor the temperature data of the water making process of the water making equipment in real time throughout its life cycle, so as to obtain real-time temperature data of the water making process;

[0084] In an embodiment of the present invention, the temperature of the water-making process of the water-making equipment throughout its life cycle is monitored in real time by using the built-in temperature sensor of the water-making equipment, so that the water temperature is monitored in real time through thermocouple or thermistor technology. Thermocouples use the voltage change between two different metals to measure temperature, while thermistors detect temperature through changes in resistance. The sensor converts the acquired temperature signal into a voltage signal, which is calibrated through an internal processing unit. The processed temperature data is regularly recorded in the memory, and finally the real-time temperature data of the water-making process is monitored.

[0085] Step S15: The real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process are transmitted to the intelligent interactive control platform through the wireless transmission network, wherein the intelligent interactive control platform includes a data processing unit, an interactive user interface and an intelligent control unit.

[0086] In an embodiment of the present invention, the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process, and the real-time temperature data of the water production process obtained by previous monitoring are transmitted to a pre-built intelligent interactive control platform through a wireless transmission network according to set time intervals or trigger conditions. The intelligent interactive control platform integrates a data processing unit, an interactive user interface, and an intelligent control unit, wherein the data processing unit processes, analyzes, and stores the received real-time data to generate detailed reports and charts; the interactive user interface allows the user to view and operate the data reports and charts in real time, and view the corresponding equipment working mode parameters; the intelligent control unit automatically adjusts the equipment operating status based on the data analysis results to achieve optimal water production control.

[0087] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in FIG. 1 , in this embodiment, step S2 includes the following steps:

[0088] Step S21: performing noise point elimination processing on the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process, and the real-time temperature data of the water production process through the data processing unit in the intelligent interactive control platform, so as to obtain pH denoised data of the water production process, particle concentration denoised data of the water production process, water flow denoised data of the water production process, and temperature denoised data of the water production process;

[0089] In an embodiment of the present invention, a noise point detection algorithm is used by a data processing unit within an intelligent interactive control platform to eliminate noise points from previously received real-time pH data of the water production process, real-time particle concentration data of the water production process, real-time water flow data of the water production process, and real-time temperature data of the water production process. In specific operations, a noise point detection method based on a median filter is used to identify and eliminate abnormal fluctuations. The median filter can effectively remove isolated noise points in the data, so that each data point is replaced by the median of its neighboring data, so that the data obtained after denoising is more stable and accurate, and finally pH denoised data of the water production process, particle concentration denoised data of the water production process, water flow denoised data of the water production process, and temperature denoised data of the water production process are obtained.

[0090] Step S22: performing histogram abnormal distribution cleaning on the pH denoised data of the water production process, the particle concentration denoised data of the water production process, the water flow denoised data of the water production process, and the temperature denoised data of the water production process to obtain pH abnormality cleaning data of the water production process, particle concentration abnormality cleaning data of the water production process, water flow abnormality cleaning data of the water production process, and temperature abnormality cleaning data of the water production process;

[0091] In an embodiment of the present invention, the abnormal distribution of the histogram of the water production process pH denoised data, the water production process particle concentration denoised data, the water flow rate denoised data and the water production process temperature denoised data previously obtained after denoising is cleaned, so as to generate a histogram using a statistical analysis tool, and identify the outlier distribution points in the distribution. The outliers are eliminated according to their frequency distribution and standard deviation to ensure the uniformity of the data set. For example, by setting a threshold range, the values ​​in the data that exceed the range are marked as abnormal, and these abnormal data points are removed, thereby reflecting the measurement values ​​within the normal range, and finally obtaining the water production process pH abnormal cleaning data, the water production process particle concentration abnormal cleaning data, the water flow rate abnormal cleaning data and the water production process temperature abnormal cleaning data.

[0092] Step S23: standardizing the water production process pH abnormality cleaning data, the water production process particle concentration abnormality cleaning data, the water production process water flow abnormality cleaning data, and the water production process temperature abnormality cleaning data to obtain water production process pH standard data, water production process particle concentration standard data, water production process water flow standard data, and water production process temperature standard data;

[0093] In the embodiment of the present invention, by using the Z-score standardization method, the mean of the data set is subtracted from each data point in the water production process pH abnormality cleaning data, water production process particle concentration abnormality cleaning data, water production process water flow abnormality cleaning data, and water production process temperature abnormality cleaning data obtained after abnormality cleaning, and then divided by the standard deviation, so that the data is converted into a standard normal distribution with a mean of 0 and a standard deviation of 1. This process uses the standardization formula: ,in is the original data point, is the mean, is the standard deviation. The standardized data are pH standard data, particle concentration standard data, water flow standard data and temperature standard data. These data are more convenient for subsequent comparison and analysis, and finally the pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process and the temperature standard data of the water production process are obtained.

[0094] Step S24: performing time series gap filling and synchronization processing on the pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process, and the temperature standard data of the water production process, so as to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process, and the temperature data of the water production process in the same time series dimension;

[0095] In an embodiment of the present invention, the time series gap filling and synchronization processing is performed on the previously standardized pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process, and the temperature standard data of the water production process, so as to apply an interpolation algorithm, such as linear interpolation or spline interpolation, to fill the missing data points in the time series gaps, so as to ensure that all pH data, particle concentration data, water flow data and temperature data are aligned in the same time dimension. In the specific operation, interpolation calculation is performed on each data sequence, and the continuity of the time series is maintained during the insertion of missing values, so as to finally obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process and the temperature data of the water production process in the same time series dimension.

[0096] Step S25: Based on the pH data and the temperature data of the water production process, the particle concentration data of the water production process is corrected for the water turbidity effect to obtain the water turbidity data of the water production process.

[0097] In an embodiment of the present invention, the water quality turbidity influence correction calculation is performed on the corresponding water production process particle concentration data by combining the water production process pH data and the water production process temperature data previously obtained after time series synchronization, so as to perform regression analysis by applying a linear regression model, taking the pH data and the temperature data as independent variables and the particle concentration data as the dependent variable, and adjusting the particle concentration data through the coefficients of the regression model, that is, the corresponding pH change influencing factors and the temperature change influencing factors, to eliminate the influence of pH and temperature on the particle concentration, thereby obtaining the corrected water quality turbidity data, which reflects the actual water quality situation after excluding the pH and temperature factors, and finally obtaining the water quality turbidity data of the water production process.

[0098] Further, step S24 includes the following steps:

[0099] Step S241: dividing the pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process, and the temperature standard data of the water production process into a set of time series point windows to obtain a subset of the water production process monitoring parameter data at each time series point;

[0100] In an embodiment of the present invention, by dividing the pH standard data, particle concentration standard data, water flow standard data and temperature standard data into time series point windows, this process involves dividing the original data into multiple time windows according to the time series, and each time window contains a data segment of a specific time length. For example, if a time window of 30 minutes is selected, all adjacent 30-minute segments are extracted from the entire data set as a time series point. In specific operations, a time series analysis tool (such as Python's Pandas library) can be used for data segmentation, and the data set is segmented according to timestamps to obtain data subsets for each time period. These data subsets will be used for subsequent missing data analysis. The division of each time series point will form a data subset, including pH data, particle concentration data, water flow data and temperature data in this time period, and finally a data subset of water production process monitoring parameters at each time series point is obtained.

[0101] Step S242: performing data missing identification analysis on a subset of water production process monitoring parameter data at each time sequence point to obtain missing sub-data of the water production process at each time sequence point;

[0102] In an embodiment of the present invention, by performing missing data identification and analysis on the data subset at each time series point, this step includes detecting whether there are missing values ​​(i.e., missing parts of the data) in each time window. In specific operations, a data analysis tool (such as Python's NumPy library) can be used to check each data subset to identify missing time sub-nodes. For each data subset at each time series point, pH data, particle concentration data, water flow data, and temperature data are traversed, missing time sub-nodes are marked, and an information subset containing missing data time sub-nodes is formed. This process involves detailed inspection and marking of the data in each time window to ensure accurate identification of missing data, and ultimately obtains the missing sub-data of the water production process at each time series point.

[0103] Step S243: performing arithmetic mean calculation of the missing point gap of the missing sub-data of the water production process at each time sequence point to obtain the missing point gap average value of the water production process at each time sequence point;

[0104] In an embodiment of the present invention, by performing arithmetic mean calculation of the missing point gaps for the missing sub-data at each time series point, in specific implementation, the time gaps between adjacent missing data time sub-nodes are calculated from the identified missing data time sub-nodes, and the average value of these gaps is obtained. For example, for the missing points of pH data, the time difference between every two adjacent missing points is calculated, and then the corresponding pH data within these time differences are arithmetic averaged to obtain the average gap value within the time window. This process can be implemented using time series processing tools (such as Python's SciPy library), and ultimately the average value of the missing point gaps in the water production process at each time series point is obtained.

[0105] Step S244: Based on the average value of the gap between missing points in the water production process at each time point, the corresponding missing sub-data in the pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process and the temperature standard data of the water production process are synchronously processed to fill the time gaps, so as to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process and the temperature data of the water production process in the same time dimension.

[0106] In an embodiment of the present invention, the missing sub-data in each monitoring parameter (pH, particle concentration, water flow rate and temperature) of the water production process are synchronously filled with missing sub-data by combining the gap average value of the missing points of the water production process at each time series point obtained by previous quantitative calculation, so as to fill the corresponding missing values. In the specific operation, the aforementioned gap average value of the missing points is used to fill the missing data through an interpolation method (such as linear interpolation or spline interpolation). In this process, the average gap value of each time series point is used to infer the possible value of the missing point, and the corresponding data position is filled. The filling operation will ensure the consistency of the data in the same time series dimension, so that the missing parts of the pH data, particle concentration data, water flow data and temperature data in the same time series dimension are reasonably filled. The processing tool may include a data interpolation library (such as the interpolation function in the scikit-learn library of Python) to ensure the synchronous processing of each data in the time series, and finally obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process and the temperature data of the water production process in the same time series dimension.

[0107] Further, step S25 includes the following steps:

[0108] Step S251: performing particle concentration spatial distribution analysis on the particle concentration data of the water production process to obtain a particle concentration spatial distribution map of the water production process;

[0109] In an embodiment of the present invention, a geographic information system (GIS) or a similar data visualization tool is used to perform a statistical analysis on the spatial distribution of the particle concentration data of the water production process obtained after time series synchronization, so as to map the collected particle concentration data to spatial coordinates, thereby generating a spatial distribution map of the particle concentration. This distribution map shows the distribution of particle concentrations at different locations and can be used to identify high-concentration areas and low-concentration areas, ensuring that the data collection frequency and the arrangement of distribution points can accurately reflect the changes in particle concentration in the water production process, so as to obtain an accurate spatial distribution map, and finally obtain a spatial distribution map of particle concentration in the water production process.

[0110] Step S252: Perform particle spatial distribution area planning analysis on the particle concentration spatial distribution diagram of the water production process to obtain the size of the particle concentration distribution area of ​​the water production process;

[0111] In an embodiment of the present invention, a statistical analysis of the spatial distribution area of ​​particles is performed on the spatial distribution map of particle concentration in the water production process obtained by the previous analysis by using image processing technology or GIS tools, so as to segment the particle concentration area in the map, and divide different particle concentration areas by adopting regional analysis methods (such as segmentation algorithm or regional growth algorithm), and calculate the area of ​​each area. This process can be achieved by performing image segmentation on the spatial distribution map and calculating the pixel area of ​​each area, and finally the area size of the particle concentration distribution area in the water production process is obtained.

[0112] Step S253: performing an initial quantitative calculation of regional water quality turbidity on the particle concentration data of the water production process based on the size of the particle concentration distribution area of ​​the water production process, so as to obtain initial turbidity data of regional water quality of the water production process;

[0113] In an embodiment of the present invention, the regional turbidity is quantitatively calculated for the corresponding water production process particle concentration data by combining the area size of the particle concentration distribution area in the water production process obtained by previous analysis, so as to combine the particle concentration data with the regional area data, and use a formula to perform preliminary turbidity quantification of water quality. This quantitative calculation takes into account the particle concentration level in each area and its corresponding area size, where the calculation formula is usually the product of the particle concentration in the area and the regional area. The results of all areas are then summarized to finally obtain the initial turbidity data of regional water quality in the water production process.

[0114] Step S254: Based on the pH data of the water production process and the temperature data of the water production process, the particle concentration spatial distribution diagram of the water production process is evaluated and analyzed to obtain the pH change-particle distribution influence factor of the water production process and the temperature change-particle distribution influence factor of the water production process;

[0115] In an embodiment of the present invention, the corresponding particle concentration distribution in the particle concentration spatial distribution diagram of the water production process is evaluated and analyzed by combining the pH data of the water production process and the temperature data of the water production process obtained by the previous analysis, so as to evaluate and analyze the influence of these factors on the particle concentration distribution. First, the pH data and the temperature data are compared with the particle concentration spatial distribution diagram, and the influencing factors of pH change and temperature change on particle distribution are determined through regression analysis or correlation analysis. These factors are applied to the particle concentration spatial distribution diagram, and the change of particle concentration under each condition is calculated, and finally the water production process pH change-particle distribution influencing factor and the water production process temperature change-particle distribution influencing factor are obtained.

[0116] Step S255: Based on the pH change-particle distribution influencing factor of the water production process and the temperature change-particle distribution influencing factor of the water production process, the water quality turbidity impact correction calculation is performed on the initial turbidity data of the water quality in the water production process area to obtain the water quality turbidity data of the water production process.

[0117] In an embodiment of the present invention, a correction calculation is performed on the initial turbidity data of water quality in the water production process area obtained by previous quantitative calculation by combining the pH change-particle distribution influence factor of the water production process and the temperature change-particle distribution influence factor of the water production process obtained by previous evaluation and analysis, so that the pH change-particle distribution influence factor and the temperature change-particle distribution influence factor are combined with the initial turbidity for correction calculation. This correction calculation adjusts the initial data according to the influence factor to reflect the actual water quality turbidity, and uses the corresponding calculation formula to perform a weighted correction on the initial turbidity data (that is, the product of the corresponding initial turbidity and the ratio between the pH change-particle distribution influence factor of the water production process and the temperature change-particle distribution influence factor of the water production process), ensuring that the water quality turbidity data finally obtained accurately reflects the influence of changes in pH and temperature in the water production process, and finally the water quality turbidity data of the water production process is obtained by correction calculation.

[0118] Further, step S254 includes the following steps:

[0119] Perform frequency change multi-scale characteristic analysis on pH data of water production process and temperature data of water production process to obtain multi-scale characteristic data of frequency change of pH of water production process and multi-scale characteristic data of frequency change of temperature of water production process;

[0120] In an embodiment of the present invention, a frequency change multi-scale feature analysis is performed on the pH data of the water production process and the temperature data of the water production process obtained after time synchronization. The specific method is to use wavelet transform (such as continuous wavelet transform or discrete wavelet transform) to decompose the original pH and temperature data to extract their features at different frequency scales. For the pH data, wavelet transform is applied to decompose the data into different frequency sub-bands, the frequency change characteristics of each sub-band are recorded, and statistics such as mean, variance and skewness are calculated at each scale. Similarly, the temperature data is processed in the same way, and finally the multi-scale feature data of the frequency change of pH in the water production process and the multi-scale feature data of the frequency change of temperature in the water production process are obtained.

[0121] Preferably, based on the multi-scale characteristic data of pH frequency change in the water production process and the multi-scale characteristic data of temperature frequency change in the water production process, the local variability evaluation analysis of the spatial distribution diagram of particle concentration in the water production process is performed to obtain the spatial variability data of pH frequency change-particle concentration local distribution and the spatial variability data of temperature frequency change-particle concentration local distribution;

[0122] In an embodiment of the present invention, the local variability of the spatial distribution map of the particle concentration in the water production process is evaluated and analyzed by combining the multi-scale characteristic data of the frequency changes of pH and temperature obtained by previous analysis, so as to calculate the variability of the particle concentration at different spatial positions by combining the characteristic data of the pH and temperature data using a local variability analysis method (such as local weighted regression or spatial variation function). Specifically, the particle concentration data is combined with the frequency change characteristics of pH and temperature, and a spatial variability map is generated by a spatial interpolation method (such as Kriging interpolation), and the corresponding local distribution data is analyzed therefrom to show the local spatial distribution variability of the particle concentration due to the frequency change of pH and the local spatial distribution variability of the particle concentration due to the frequency change of temperature, and finally the pH frequency change-particle concentration local distribution spatial variability data and the temperature frequency change-particle concentration local distribution spatial variability data are obtained.

[0123] Preferably, a spatial correlation analysis is performed on the pH frequency change-particle concentration local distribution spatial variability data and the temperature frequency change-particle concentration local distribution spatial variability data to obtain the spatial correlation relationship between the pH frequency change and the particle concentration distribution position and the spatial correlation relationship between the temperature frequency change and the particle concentration distribution position;

[0124] In an embodiment of the present invention, a spatial correlation statistical analysis is performed on the spatial variability data of pH frequency change-particle concentration local distribution and the spatial variability data of temperature frequency change-particle concentration local distribution obtained previously by using a spatial correlation analysis tool (such as Moran's index or local spatial autocorrelation analysis) to statistically calculate the spatial correlation between pH and temperature frequency changes and particle concentration distribution. Specifically, the local variability data is correlated with the spatial coordinates, and the correlation coefficient or spatial autocorrelation coefficient is calculated to reveal the spatial relationship between the pH frequency change and the temperature frequency change and the particle concentration distribution position, and finally the spatial correlation relationship between the pH frequency change and the particle concentration distribution position and the spatial correlation relationship between the temperature frequency change and the particle concentration distribution position are obtained.

[0125] Preferably, based on the spatial correlation relationship between pH frequency change and particle concentration distribution position, the pH change dynamic impact assessment analysis is performed on the particle concentration spatial distribution diagram of the water production process to obtain the pH change-particle distribution impact factor of the water production process;

[0126] In an embodiment of the present invention, the dynamic impact of pH changes is evaluated and analyzed by combining the spatial correlation relationship between pH frequency changes and particle concentration distribution positions obtained in the previous analysis to analyze the corresponding spatial distribution diagram of particle concentration in the water production process. A regression model (such as a generalized additive model) is established to take the spatial correlation of pH frequency changes as the independent variable and the spatial distribution of particle concentration as the dependent variable for analysis. The model can analyze the dynamic influencing factors of pH changes and predict the dynamic impact of pH changes on particle concentration distribution. This factor shows the specific degree of influence of pH changes on particle concentration distribution, and finally the pH change-particle distribution influencing factor of the water production process is obtained.

[0127] Preferably, based on the spatial correlation relationship between temperature frequency change and particle concentration distribution position, the spatial distribution diagram of particle concentration in the water production process is subjected to a temperature change dynamic impact assessment analysis to obtain a water production process temperature change-particle distribution impact factor.

[0128] In an embodiment of the present invention, the dynamic impact of temperature change is evaluated and analyzed by combining the spatial correlation relationship between the temperature frequency change and the particle concentration distribution position obtained in the previous analysis with the corresponding spatial distribution diagram of the particle concentration in the water production process. A similar regression analysis method is used to take the spatial correlation of the temperature frequency change as the independent variable and the spatial distribution of the particle concentration as the dependent variable for modeling. The dynamic influencing factor of temperature change is analyzed by modeling, and the impact of temperature change on the particle concentration distribution is predicted. This factor describes how temperature change dynamically affects the spatial distribution of particle concentration, and finally the temperature change-particle distribution influencing factor of the water production process is obtained.

[0129] Further, step S3 includes the following steps:

[0130] Step S31: Visualizing the data distribution of water quality turbidity data, water flow data and temperature data of the water production process through an interactive user interface in the intelligent interactive control platform to generate a water quality turbidity change curve, a water flow change curve and a temperature change curve;

[0131] In an embodiment of the present invention, water turbidity, water flow rate and temperature data in the water production process are received by using an interactive user interface in an intelligent interactive control platform. These data should be collected in real time or periodically by a data acquisition sensor, and preliminarily processed and cleaned in a data processing unit. Subsequently, these data are plotted into a chart by using a data visualization tool in the interactive user interface (such as the Matplotlib library in Python or commercial data visualization software such as Tableau). Changes in water turbidity data should be plotted as a water turbidity change curve, water flow data should be plotted as a water flow change curve, and temperature data should be plotted as a temperature change curve. These curves should be able to accurately reflect the trend of data changes over time and mark important time nodes. Finally, corresponding water turbidity change curves, water flow change curves and temperature change curves are generated.

[0132] Step S32: performing a mapping conversion of the water quality turbidity change curve, the water flow change curve and the temperature change curve in the same coordinate system to obtain a water quality turbidity curve, a water flow curve and a temperature curve in the same time series coordinate system;

[0133] In an embodiment of the present invention, the drawn water quality turbidity change curve, water flow change curve and temperature change curve are mapped to the same coordinate system by using time series data processing technology to ensure that the data points of all curves have the same timestamp or perform time alignment processing to ensure the consistency of the time series, and use a data alignment algorithm (such as interpolation or resampling) to convert the curve to a unified time axis. In this process, the time series processing function in the data processing software or programming language can be used to ensure that all data are compared and analyzed on the same time scale, and finally the water quality turbidity curve, water flow curve and temperature curve in the same time series coordinate system are obtained.

[0134] Step S33: performing curve change trend recognition analysis on the water quality turbidity curve, water flow curve and temperature curve in the same time series coordinate system to obtain the water quality turbidity curve change trend, water flow curve change trend and temperature curve change trend in the same time series coordinate system;

[0135] In an embodiment of the present invention, by performing change trend identification analysis on the water quality turbidity curve, the water flow curve and the temperature curve in the same time series coordinate system, each curve is fitted by using a trend analysis tool (such as the curve fitting function in the SciPy library in Python) to identify the trend line of the water production process monitoring data. This step includes calculating the derivative of each curve to identify the rate of increase or decrease, and using a sliding average or other smoothing technique to reduce the impact of noise. By analyzing these trend lines, the change trend of each curve is obtained, including growth, decrease or stable stages, so as to reveal the overall change law of the data, and finally the change trend of the water quality turbidity curve, the change trend of the water flow curve and the change trend of the temperature curve in the same time series coordinate system are obtained.

[0136] Step S34: determining the water production mode time interval of the corresponding water quality turbidity curve, water flow curve and temperature curve based on the change trend of the water quality turbidity curve, the change trend of the water flow curve and the change trend of the temperature curve in the same time series coordinate system, so as to obtain the curve time series intervals of different water production process working mode stages, wherein the different water production process working mode stages include reverse osmosis water production mode, distillation water production mode, ion exchange water production mode, ultrafiltration water production mode, nanofiltration water production mode and ultraviolet disinfection water production mode;

[0137] In an embodiment of the present invention, by combining the water quality turbidity curve change trend, water flow curve change trend and temperature curve change trend obtained by previous identification and analysis in the same time series coordinate system, the corresponding water quality turbidity curve, water flow curve and temperature curve are identified and determined for different water production mode time periods, so as to identify the typical time periods of each water production mode by marking the characteristic points of trend changes. For example, in the reverse osmosis water production mode, the turbidity continues to decrease, the water flow rate rises from a low level, and the temperature fluctuates around normal temperature; in the distillation water production mode, the turbidity decreases slowly from a low level, the water flow rate rises slowly from a low level, and the temperature continues to rise; in the ion exchange water production mode, the turbidity fluctuates steadily. The water flow rate continues to rise, while the temperature fluctuates within the normal temperature range; in the ultrafiltration water-making mode, the turbidity drops significantly, the water flow rate continues to rise, and the temperature fluctuates within the normal temperature range; in the nanofiltration water-making mode, the median turbidity drops, the median water flow rate rises slowly, and the temperature fluctuates within the normal temperature range; in the ultraviolet disinfection water-making mode, the turbidity fluctuates slightly, the water flow rate rises irregularly, and the temperature fluctuates within the normal temperature range. Using these trend characteristics, the entire time series can be divided into multiple intervals, each interval corresponds to a water-making mode, and the basis for division can include significant change points and stable stages of the trend, and finally the curve time series intervals of different working mode stages of the water-making process are obtained.

[0138] Step S35: Based on the curve time series division intervals of different water production process working mode stages, the water quality turbidity curve change trend, water flow curve change trend and temperature curve change trend in the same time series coordinate system are divided into water production mode stages to obtain the water quality parameter change status under different water production process working mode stages.

[0139] In an embodiment of the present invention, by combining the previously obtained curve time series intervals of different water production process working mode stages, the corresponding water quality turbidity curve change trend, water flow curve change trend and temperature curve change trend are divided into detailed water production mode stages. In this process, it is necessary to use data segmentation and interval division technology (such as K-means clustering algorithm) to divide the data into multiple stages, each stage corresponds to a specific water production mode, and by analyzing the curve characteristics of each stage, the water quality parameter change status under each water production mode is determined, including the fluctuation range of water quality turbidity, the change trend of water flow and the temperature fluctuation. These data can be used to optimize the water production process and adjust the working parameters of the equipment to improve the water production effect and efficiency, and finally the water quality parameter change status under different water production process working mode stages is obtained.

[0140] Further, step S34 includes the following steps:

[0141] Based on the changing trends of the water quality turbidity curve, the water flow curve and the temperature curve in the same time series coordinate system, the changing trend identification and analysis of the water production process working mode corresponding to the water production equipment is carried out. When the changing trend of the water quality turbidity curve shows a continuous downward trend, the changing trend of the water flow curve shows a low upward trend and the changing trend of the temperature curve shows a fluctuation trend within the normal temperature range, the water production process working mode is judged to be the reverse osmosis water production mode; when the changing trend of the water quality turbidity curve shows a low and gentle downward trend, the changing trend of the water flow curve also shows a low and gentle upward trend and the changing trend of the temperature curve shows a continuous upward trend, the water production process working mode is judged to be the distillation water production mode; when the changing trend of the water quality turbidity curve shows a trend of small fluctuations, the changing trend of the water flow curve shows a continuous upward trend and the changing trend of the temperature curve shows a normal When the turbidity curve of water quality shows a significant downward trend, the water flow curve shows a continuous upward trend, and the temperature curve shows a fluctuation trend within the normal temperature range, the working mode of the water making process is determined to be the ultrafiltration water making mode; when the turbidity curve of water quality shows a median downward trend, the water flow curve shows a median slow upward trend, and the temperature curve shows a fluctuation trend within the normal temperature range, the working mode of the water making process is determined to be the nanofiltration water making mode; and when the turbidity curve of water quality shows a trend of small fluctuations, the water flow curve shows an irregular fluctuation upward trend, and the temperature curve shows a fluctuation trend within the normal temperature range, the working mode of the water making process is determined to be the ultraviolet disinfection water making mode;

[0142] In an embodiment of the present invention, a change trend identification and analysis of the working mode of the water production process is performed based on a water quality turbidity curve, a water flow curve and a temperature curve in the same time series coordinate system, so as to collect real-time data of water quality turbidity, water flow and temperature generated by the water production equipment during operation and plot them in the same time series coordinate system. Data collection can be achieved by sensors and monitoring instruments installed on the water production equipment. These sensors can measure and record data of these three parameters in real time. For the water quality turbidity curve, its change trend is analyzed to see whether it shows a continuous downward trend. The sliding average method or a linear regression model is used to smooth the curve and determine the trend. If the curve If it shows a continuous downward trend, it means that the turbidity of the water quality is gradually decreasing. For the water flow curve, determine whether it shows a low-level upward trend. Confirm this trend by calculating the increment and change rate of the water flow. If the water flow curve shows a low-level upward stage, it means that the water flow is gradually increasing, but the amplitude is small. Analyze the temperature curve to determine whether it fluctuates within the normal temperature range. The temperature fluctuation can be determined by calculating the standard deviation of the temperature data to determine whether it fluctuates within the set normal temperature range. If the water turbidity curve continues to decline, the water flow curve rises from a low position, and the temperature curve fluctuates within the normal temperature range, the working mode of the water production process is judged to be the reverse osmosis water production mode. Similarly, other water production modes are analyzed according to the above steps. When the water quality turbidity curve shows a low and gentle downward trend, the water flow curve rises slowly from a low position, and the temperature curve continues to rise, it is judged as a distillation water production mode; when the water quality turbidity curve does not fluctuate much, the water flow curve continues to rise, and the temperature curve fluctuates within the normal temperature range, it is judged as an ion exchange water production mode; when the water quality turbidity curve drops sharply, the water flow curve continues to rise, and the temperature curve fluctuates within the normal temperature range, it is judged as an ultrafiltration water production mode; when the median of the water quality turbidity curve drops, the median of the water flow curve rises slowly, and the temperature curve fluctuates within the normal temperature range, it is judged as a nanofiltration water production mode; when the water quality turbidity curve does not fluctuate much, the water flow curve fluctuates irregularly and rises, and the temperature curve fluctuates within the normal temperature range, it is judged as an ultraviolet disinfection water production mode.

[0143] Preferably, the above-mentioned water production process working mode identification process is designed as a water production equipment working mode stage division standard, and based on the water production equipment working mode stage division standard, the water quality turbidity curve, water flow curve and temperature curve in the same time coordinate system The corresponding time series are determined to determine the water production mode timing interval, so as to obtain the curve timing division intervals of different water production process working mode stages.

[0144] In an embodiment of the present invention, the previous water production process working mode discrimination process is designed into a corresponding water production equipment working mode stage division standard, and based on this standard, the water quality turbidity curve, water flow curve and temperature curve in the same time series coordinate system are used to determine the water production mode time series interval, so as to establish a curve feature template for each working mode according to the different working mode discrimination standards defined in the previous design. For example, the water quality turbidity curve of the reverse osmosis water production mode should show a continuous decline, the water flow curve should rise at a low level, and the temperature curve should be within the normal temperature fluctuation range. The water flow curve and temperature curve are subjected to pattern recognition and time series analysis. Time series analysis methods such as dynamic time warping (DTW) or long short-term memory network (LSTM) model are used to match the curve patterns and identify which working mode the corresponding time series range belongs to. The curve characteristics of each working mode are compared with the real-time data to obtain the most suitable working mode and record the corresponding time series interval. For example, if the data meets the characteristics of the reverse osmosis water production mode, the data during this period is marked as the reverse osmosis water production mode interval, and finally the curve time series intervals of the working mode stages of the water production process are obtained.

[0145] Further, step S4 includes the following steps:

[0146] Step S41: obtaining the operating status of the water production equipment in different water production process working mode stages through an interactive user interface;

[0147] In an embodiment of the present invention, an interactive user interface (UI) is used to monitor in real time the equipment operation status of the water production equipment in different working mode stages (including water production working modes such as reverse osmosis, distillation, ion exchange, ultrafiltration, nanofiltration, and ultraviolet disinfection). The UI interface graphically presents the equipment parameters and status under each working mode. For example, in the reverse osmosis mode, the interface displays information such as the pump flow rate, pressure sensor reading, and membrane flux; in the distillation mode, the interface displays data such as the temperature of the heater and the cooling effect of the condenser. These data are obtained through embedded sensors and data acquisition modules, and are transmitted to the interactive user interface in the intelligent interactive control platform through industrial communication protocols (such as Modbus and Profibus). Users monitor in real time through a touch screen or computer interface, and can select different working modes to view the corresponding operating status data, and finally obtain the operating status of the water production equipment in different working mode stages of the water production process.

[0148] Step S42: based on the operation status of the water production equipment and the change status of the water quality parameters in different water production process working mode stages, the water production process problem analysis is performed for the corresponding water production process working mode stage to obtain the problem points of the water production equipment water production process working mode;

[0149] In an embodiment of the present invention, by combining the operating conditions of the water production equipment under different working mode stages of the water production process obtained by previous analysis and the changes in water quality parameters (such as changes in turbidity, water flow, temperature, etc.), the corresponding working mode stages of the water production process are identified and analyzed for water production process problems, so that the operating data of the water production equipment under different working modes are processed and analyzed by using data analysis tools such as Pandas and NumPy libraries in Python, and the changing trends of water quality parameters over time are displayed by constructing mathematical models and data visualization tools (such as Matplotlib or Seaborn). By comparing the normal operating data and abnormal data under different working modes, the working mode stage where the problem occurs is identified. For example, if an abnormal increase in water quality parameters is observed in the ion exchange mode, it may indicate saturation or damage of the ion exchange resin. The corresponding analysis results are summarized to generate a report in which each problem point identified by the water production equipment under different working modes is recorded in detail, and finally the problem points of the water production process working mode of the water production equipment are obtained.

[0150] Step S43: performing water production process problem point response processing on the water production process working mode problem point of the water production equipment through the intelligent control unit in the intelligent interactive control platform to generate a water production process problem point response control instruction for the water production equipment;

[0151] In an embodiment of the present invention, a previously identified problem point of water production equipment is responded to and processed by using an intelligent control unit (such as a PLC or DCS system) within an intelligent interactive control platform. The intelligent control unit generates a control instruction based on a preset control strategy and the specific situation of the problem point. For example, if it is found that the pressure of the reverse osmosis membrane exceeds the normal range, the control unit will generate an instruction to adjust the water inlet flow rate or start a membrane cleaning program. The generated control instruction includes specific operating steps and parameter settings. The intelligent control unit can automatically adjust the operating parameters through an integrated algorithm (such as PID control or fuzzy control) to ensure that the equipment operates in a safe and efficient state, and finally responds to generate a control instruction in response to the problem point in the water production process of the water production equipment.

[0152] Step S44: Send a water production process problem point response control instruction to the built-in execution unit of the water production equipment through the intelligent control unit, and perform water production process problem point interactive control response to the water production process working mode problem point of the water production equipment, generate a water production process problem point interactive control strategy for the water production equipment, and execute corresponding water production process problem interactive control adjustment work.

[0153] In an embodiment of the present invention, the generated control instructions are sent to the built-in execution unit of the water production equipment (such as an electric valve, a pump speed control device, a heater control system, etc.) by using an intelligent control unit. The execution unit receives and executes these instructions, and the execution unit makes necessary adjustments to the corresponding water production process working mode problem points. For example, when an instruction to adjust the reverse osmosis membrane pressure is received, the electric valve will adjust the water inlet flow rate, and the pump speed control device will change the speed. The execution unit performs interactive control responses through a feedback mechanism (such as a sensor monitoring real-time pressure changes) to ensure the effectiveness of the adjustment measures. The interactive control strategy for the water production process problem points of the water production equipment finally generated will optimize and adjust the water production equipment, and feedback the adjustment results on the UI interface to ensure that the water production equipment can maintain a stable operating state in the subsequent operation, thereby performing the corresponding interactive control adjustment work for the water production process problems.

[0154] Furthermore, the present invention also provides a system for intelligent interactive control of water production equipment, which is used to execute the method for intelligent interactive control of water production equipment as described above, and the system for intelligent interactive control of water production equipment includes:

[0155] The water quality parameter monitoring module of the water production process is used to monitor the water production process data of the water production equipment in real time during the whole life cycle of the water production process through the built-in pH monitoring equipment, particle concentration monitoring equipment, flow sensor and temperature sensor of the water production equipment, so as to obtain the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process; the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process are transmitted to the intelligent interactive control platform through the wireless transmission network, wherein the intelligent interactive control platform includes a data processing unit, an interactive user interface and an intelligent control unit;

[0156] The water quality parameter processing module of the water production process is used to perform time series gap filling and synchronous processing on the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process through the data processing unit in the intelligent interactive control platform, so as to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process and the temperature data of the water production process in the same time series dimension; based on the pH data of the water production process and the temperature data of the water production process, the particle concentration data of the water production process is corrected and calculated for the water quality turbidity, so as to obtain the water quality turbidity data of the water production process;

[0157] The module for visualizing and dividing the water production mode of the water production process is used to visualize the data distribution of the water quality turbidity data, the water flow data and the temperature data of the water production process through the interactive user interface in the intelligent interactive control platform, so as to generate the water quality turbidity change curve, the water flow change curve and the temperature change curve; the water quality turbidity change curve, the water flow change curve and the temperature change curve are divided into water production modes according to the change trend, so as to obtain the water quality parameter change status under different water production process working mode stages, wherein the different water production process working mode stages include reverse osmosis water production mode, distillation water production mode, ion exchange water production mode, ultrafiltration water production mode, nanofiltration water production mode and ultraviolet disinfection water production mode;

[0158] The water production process problem point interactive control module is used to obtain the operating status of the water production equipment under different water production process working mode stages through an interactive user interface, and analyze the water production process problems in the corresponding water production process working mode stages based on the operating status of the water production equipment under different water production process working mode stages and the change status of water quality parameters, and obtain the water production process working mode problem points of the water production equipment; through the intelligent control unit in the intelligent interactive control platform and the built-in execution unit of the water production equipment, the water production process problem point interactive control response is performed to the water production process working mode problem points of the water production equipment, and an interactive control strategy for the water production process problem points of the water production equipment is generated to execute the corresponding water production process problem interactive control adjustment work.

[0159] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0160] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for intelligent interactive control of water production equipment, characterized in that: The following steps are involved: Step S1: real-time monitoring of the water production process data of the water production equipment during its entire life cycle is performed through the built-in pH monitoring equipment, particle concentration monitoring equipment, flow sensor and temperature sensor of the water production equipment to obtain real-time pH data of the water production process, real-time particle concentration data of the water production process, real-time water flow data of the water production process and real-time temperature data of the water production process; the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process are transmitted to the intelligent interactive control platform through a wireless transmission network, wherein the intelligent interactive control platform includes a data processing unit, an interactive user interface and an intelligent control unit; Step S2: The real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process are synchronously processed by the data processing unit in the intelligent interactive control platform to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process and the temperature data of the water production process in the same time series dimension; based on the pH data of the water production process and the temperature data of the water production process, the particle concentration data of the water production process is corrected and calculated for the water quality turbidity to obtain the water quality turbidity data of the water production process; wherein, step S2 includes the following steps: Step S21: performing noise point elimination processing on the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process, and the real-time temperature data of the water production process through the data processing unit in the intelligent interactive control platform, so as to obtain pH denoised data of the water production process, particle concentration denoised data of the water production process, water flow denoised data of the water production process, and temperature denoised data of the water production process; Step S22: performing histogram abnormal distribution cleaning on the pH denoised data of the water production process, the particle concentration denoised data of the water production process, the water flow denoised data of the water production process, and the temperature denoised data of the water production process to obtain pH abnormality cleaning data of the water production process, particle concentration abnormality cleaning data of the water production process, water flow abnormality cleaning data of the water production process, and temperature abnormality cleaning data of the water production process; Step S23: standardizing the water production process pH abnormality cleaning data, the water production process particle concentration abnormality cleaning data, the water production process water flow abnormality cleaning data, and the water production process temperature abnormality cleaning data to obtain water production process pH standard data, water production process particle concentration standard data, water production process water flow standard data, and water production process temperature standard data; Step S24: performing time series gap filling and synchronization processing on the pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process, and the temperature standard data of the water production process, so as to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process, and the temperature data of the water production process in the same time series dimension; Step S25: Based on the pH data and the temperature data of the water production process, the particle concentration data of the water production process is corrected for the water turbidity effect to obtain the water turbidity data of the water production process; wherein step S25 includes the following steps: Step S251: performing particle concentration spatial distribution analysis on the particle concentration data of the water production process to obtain a particle concentration spatial distribution map of the water production process; Step S252: Perform particle spatial distribution area planning analysis on the particle concentration spatial distribution diagram of the water production process to obtain the size of the particle concentration distribution area of ​​the water production process; Step S253: performing an initial quantitative calculation of regional water quality turbidity on the particle concentration data of the water production process based on the size of the particle concentration distribution area of ​​the water production process, so as to obtain initial turbidity data of regional water quality of the water production process; Step S254: Based on the pH data of the water production process and the temperature data of the water production process, the particle concentration spatial distribution diagram of the water production process is evaluated and analyzed to obtain the pH change-particle distribution influence factor of the water production process and the temperature change-particle distribution influence factor of the water production process; wherein step S254 includes the following steps: Perform frequency change multi-scale characteristic analysis on pH data of water production process and temperature data of water production process to obtain multi-scale characteristic data of frequency change of pH of water production process and multi-scale characteristic data of frequency change of temperature of water production process; Based on the multi-scale characteristic data of pH frequency changes in the water production process and the multi-scale characteristic data of temperature frequency changes in the water production process, the local variability evaluation and analysis of the spatial distribution map of particle concentration in the water production process was carried out to obtain the spatial variability data of pH frequency change-particle concentration local distribution and the spatial variability data of temperature frequency change-particle concentration local distribution; Perform spatial correlation analysis on the pH frequency change-particle concentration local distribution spatial variability data and the temperature frequency change-particle concentration local distribution spatial variability data to obtain the spatial correlation relationship between the pH frequency change and the particle concentration distribution position and the spatial correlation relationship between the temperature frequency change and the particle concentration distribution position; Based on the spatial correlation between pH frequency change and particle concentration distribution position, the dynamic impact of pH change on the particle concentration spatial distribution map of the water production process is evaluated and analyzed to obtain the pH change-particle distribution influencing factor of the water production process; Based on the spatial correlation between temperature frequency change and particle concentration distribution position, the dynamic impact of temperature change on the particle concentration spatial distribution diagram of the water production process is evaluated and analyzed to obtain the temperature change-particle distribution impact factor of the water production process; Step S255: performing water quality turbidity impact correction calculation on the initial turbidity data of water quality in the water production process area based on the pH change-particle distribution influence factor of the water production process and the temperature change-particle distribution influence factor of the water production process to obtain the water quality turbidity data of the water production process; Step S3: Visualize the data distribution of water quality turbidity data, water flow data and temperature data of the water production process through the interactive user interface in the intelligent interactive control platform to generate water quality turbidity change curves, water flow change curves and temperature change curves; divide the water quality turbidity change curves, water flow change curves and temperature change curves into water production modes according to change trends to obtain the water quality parameter change conditions under different water production process working mode stages, wherein the different water production process working mode stages include reverse osmosis water production mode, distillation water production mode, ion exchange water production mode, ultrafiltration water production mode, nanofiltration water production mode and ultraviolet disinfection water production mode; Step S4: Obtain the operating status of the water-making equipment under different water-making process working mode stages through an interactive user interface, and perform water-making process problem analysis on the corresponding water-making process working mode stages based on the operating status of the water-making equipment under different water-making process working mode stages and the change status of water quality parameters, so as to process and analyze the operating data of the water-making equipment under different working modes by using the Pandas and NumPy libraries in the data analysis tool Python, and display the changing trend of water quality parameters over time by building mathematical models and data visualization tools, identify the working mode with problems by comparing the normal operating data and abnormal data under different working modes, and obtain the problem points of the water-making process working mode of the water-making equipment; perform interactive control response of the water-making process problem points to the water-making equipment working mode problem points through the intelligent control unit in the intelligent interactive control platform and the built-in execution unit of the water-making equipment, generate an interactive control strategy for the water-making process problem points of the water-making equipment, and perform corresponding interactive control adjustment work on the water-making process problems.

2. The method for intelligent interactive control of water production equipment according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Real-time monitoring of pH data of the water production process of the water production equipment throughout its life cycle is performed using a pH monitoring device built into the water production equipment to obtain real-time pH data of the water production process; Step S12: using a particle concentration monitoring device built into the water production equipment to monitor particle concentration data in real time during the entire life cycle of the water production equipment, so as to obtain real-time particle concentration data of the water production process; Step S13: using a built-in flow sensor of the water production equipment to monitor the water flow data of the water production equipment in real time during the entire life cycle of the water production equipment, so as to obtain real-time water flow data of the water production process; Step S14: using a temperature sensor built into the water making equipment to monitor the temperature data of the water making process of the water making equipment in real time throughout its life cycle, so as to obtain real-time temperature data of the water making process; Step S15: The real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process are transmitted to the intelligent interactive control platform through the wireless transmission network, wherein the intelligent interactive control platform includes a data processing unit, an interactive user interface and an intelligent control unit.

3. The method for intelligent interactive control of water production equipment according to claim 1, characterized in that: Step S24 includes the following steps: Step S241: dividing the pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process, and the temperature standard data of the water production process into a set of time series point windows to obtain a subset of the water production process monitoring parameter data at each time series point; Step S242: performing data missing identification analysis on a subset of water production process monitoring parameter data at each time sequence point to obtain missing sub-data of the water production process at each time sequence point; Step S243: performing arithmetic mean calculation of the missing point gap of the missing sub-data of the water production process at each time sequence point to obtain the missing point gap average value of the water production process at each time sequence point; Step S244: Based on the average value of the gap between missing points in the water production process at each time point, the corresponding missing sub-data in the pH standard data of the water production process, the particle concentration standard data of the water production process, the water flow standard data of the water production process and the temperature standard data of the water production process are synchronously processed to fill the time gaps, so as to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process and the temperature data of the water production process in the same time dimension.

4. The method for intelligent interactive control of water production equipment according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Visualizing the data distribution of water quality turbidity data, water flow data and temperature data of the water production process through an interactive user interface in the intelligent interactive control platform to generate a water quality turbidity change curve, a water flow change curve and a temperature change curve; Step S32: performing a mapping conversion of the water quality turbidity change curve, the water flow change curve and the temperature change curve in the same coordinate system to obtain a water quality turbidity curve, a water flow curve and a temperature curve in the same time series coordinate system; Step S33: performing curve change trend recognition analysis on the water quality turbidity curve, water flow curve and temperature curve in the same time series coordinate system to obtain the water quality turbidity curve change trend, water flow curve change trend and temperature curve change trend in the same time series coordinate system; Step S34: determining the water production mode time interval of the corresponding water quality turbidity curve, water flow curve and temperature curve based on the change trend of the water quality turbidity curve, the change trend of the water flow curve and the change trend of the temperature curve in the same time series coordinate system, so as to obtain the curve time series intervals of different water production process working mode stages, wherein the different water production process working mode stages include reverse osmosis water production mode, distillation water production mode, ion exchange water production mode, ultrafiltration water production mode, nanofiltration water production mode and ultraviolet disinfection water production mode; Step S35: Based on the curve time series division intervals of different water production process working mode stages, the water quality turbidity curve change trend, water flow curve change trend and temperature curve change trend in the same time series coordinate system are divided into water production mode stages to obtain the water quality parameter change status under different water production process working mode stages.

5. The method for intelligent interactive control of water production equipment according to claim 4, characterized in that: Step S34 includes the following steps: Based on the changing trends of the water quality turbidity curve, the water flow curve and the temperature curve in the same time series coordinate system, the changing trend identification and analysis of the water production process working mode corresponding to the water production equipment is carried out. When the changing trend of the water quality turbidity curve shows a continuous downward trend, the changing trend of the water flow curve shows a low upward trend and the changing trend of the temperature curve shows a fluctuation trend within the normal temperature range, the water production process working mode is judged to be the reverse osmosis water production mode; when the changing trend of the water quality turbidity curve shows a low and gentle downward trend, the changing trend of the water flow curve also shows a low and gentle upward trend and the changing trend of the temperature curve shows a continuous upward trend, the water production process working mode is judged to be the distillation water production mode; when the changing trend of the water quality turbidity curve shows a trend of small fluctuations, the changing trend of the water flow curve shows a continuous upward trend and the changing trend of the temperature curve shows a normal When the turbidity curve of water quality shows a significant downward trend, the water flow curve shows a continuous upward trend, and the temperature curve shows a fluctuation trend within the normal temperature range, the working mode of the water making process is determined to be the ultrafiltration water making mode; when the turbidity curve of water quality shows a median downward trend, the water flow curve shows a median slow upward trend, and the temperature curve shows a fluctuation trend within the normal temperature range, the working mode of the water making process is determined to be the nanofiltration water making mode; and when the turbidity curve of water quality shows a trend of small fluctuations, the water flow curve shows an irregular fluctuation upward trend, and the temperature curve shows a fluctuation trend within the normal temperature range, the working mode of the water making process is determined to be the ultraviolet disinfection water making mode; The above-mentioned water production process working mode discrimination process is designed as the water production equipment working mode stage division standard, and based on the water production equipment working mode stage division standard, the water quality turbidity curve, water flow curve and temperature curve in the same time coordinate system are used to determine the water production mode timing interval to obtain the curve timing division intervals of different water production process working mode stages.

6. The method for intelligent interactive control of water production equipment according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: obtaining the operating status of the water production equipment in different water production process working mode stages through an interactive user interface; Step S42: based on the operation status of the water production equipment and the change status of the water quality parameters in different water production process working mode stages, the water production process problem analysis is performed for the corresponding water production process working mode stage to obtain the problem points of the water production equipment water production process working mode; Step S43: performing water production process problem point response processing on the water production process working mode problem point of the water production equipment through the intelligent control unit in the intelligent interactive control platform to generate a water production process problem point response control instruction for the water production equipment; Step S44: Send a water production process problem point response control instruction to the built-in execution unit of the water production equipment through the intelligent control unit, and perform water production process problem point interactive control response to the water production process working mode problem point of the water production equipment, generate a water production process problem point interactive control strategy for the water production equipment, and execute corresponding water production process problem interactive control adjustment work.

7. A system for intelligent interactive control of water production equipment, characterized in that: A method for executing the method of intelligent interactive control of water production equipment according to claim 1, wherein the system of intelligent interactive control of water production equipment comprises: The water quality parameter monitoring module of the water production process is used to monitor the water production process data of the water production equipment in real time during the whole life cycle of the water production process through the built-in pH monitoring equipment, particle concentration monitoring equipment, flow sensor and temperature sensor of the water production equipment, so as to obtain the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process; the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process are transmitted to the intelligent interactive control platform through the wireless transmission network, wherein the intelligent interactive control platform includes a data processing unit, an interactive user interface and an intelligent control unit; The water quality parameter processing module of the water production process is used to perform time series gap filling and synchronous processing on the real-time pH data of the water production process, the real-time particle concentration data of the water production process, the real-time water flow data of the water production process and the real-time temperature data of the water production process through the data processing unit in the intelligent interactive control platform, so as to obtain the pH data of the water production process, the particle concentration data of the water production process, the water flow data of the water production process and the temperature data of the water production process in the same time series dimension; based on the pH data of the water production process and the temperature data of the water production process, the particle concentration data of the water production process is corrected and calculated for the water quality turbidity, so as to obtain the water quality turbidity data of the water production process; The module for visualizing and dividing the water production mode of the water production process is used to visualize the data distribution of the water quality turbidity data, the water flow data and the temperature data of the water production process through the interactive user interface in the intelligent interactive control platform, so as to generate the water quality turbidity change curve, the water flow change curve and the temperature change curve; the water quality turbidity change curve, the water flow change curve and the temperature change curve are divided into water production modes according to the change trend, so as to obtain the water quality parameter change status under different water production process working mode stages, wherein the different water production process working mode stages include reverse osmosis water production mode, distillation water production mode, ion exchange water production mode, ultrafiltration water production mode, nanofiltration water production mode and ultraviolet disinfection water production mode; The water production process problem point interactive control module is used to obtain the operating status of the water production equipment under different water production process working mode stages through an interactive user interface, and analyze the water production process problems in the corresponding water production process working mode stages based on the operating status of the water production equipment under different water production process working mode stages and the change status of water quality parameters, and obtain the water production process working mode problem points of the water production equipment; through the intelligent control unit in the intelligent interactive control platform and the built-in execution unit of the water production equipment, the water production process problem point interactive control response is performed to the water production process working mode problem points of the water production equipment, and an interactive control strategy for the water production process problem points of the water production equipment is generated to execute the corresponding water production process problem interactive control adjustment work.

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