A cloud platform intelligent control method of a water purifier

Abstract

The application discloses a kind of cloud platform intelligent control methods of water purifier, including following steps: S1, complete terminal water purifier equipment network binding, identity authentication and data interaction adaptation;S2, real-time acquisition equipment operation data, water quality working condition data;S3, terminal water purifier will the data collected in step S2 be uploaded to cloud intelligent management and control platform and carry out cleaning, noise reduction, real-time analysis;S4, dynamically self-adapting adjustment pulse knock frequency, knock duration;S5, real-time monitoring equipment operation abnormal data;S6, realize the remote start-stop of terminal water purifier, mode switching, parameter customization, timing water production function, and form individualized water control archives.The application completely solves the industry pain point that traditional equipment water quality adaptability is poor and water resource is wasted seriously, realizes water purification effect and water-saving performance, adapts to the needs of multi-scene large-scale intelligent operation and maintenance of family, park, commercial office and the like.

Description

Technical Field

[0001] This invention relates to the field of water purifier technology, and in particular to a cloud platform intelligent control method for water purifiers. Background Technology

[0002] With the rapid development of smart home IoT technology, smart water purification equipment has been widely used in various water use scenarios such as homes, parks, and commercial offices. Users are increasingly demanding more from water purification equipment in terms of water quality stability, intelligent management and control, water-saving and energy-saving performance, and ease of operation and maintenance.

[0003] While mainstream IoT water purifiers on the market can achieve simple data uploading and remote viewing functions, their overall control logic still adopts the traditional localized, fixed-parameter water control mode. This results in many technical defects such as low intelligence, poor adaptability to working conditions, easy equipment wear and tear, and lagging maintenance, making it difficult to meet the needs of refined, adaptive, and highly stable water purification. Furthermore, existing cloud-based water purification management systems only provide basic data transmission and simple remote start / stop functions, with weak data processing capabilities. The water quality and operating condition data collected by the equipment are easily affected by external interference such as pump vibration, voltage fluctuations, and water flow impact in pipelines, resulting in high-frequency noise and abnormal data. Traditional systems lack professional data cleaning and time-series noise reduction mechanisms, and abnormal dirty data directly participates in equipment control and judgment, which can easily lead to problems such as misadjustment of water production parameters, false fault warnings, and inaccurate water quality assessment. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a cloud platform intelligent control method for water purifiers.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a cloud platform intelligent control method for a water purifier, comprising the following steps: S1. Establish an IoT communication link between the cloud-based intelligent management and control platform and each terminal water purifier, and complete the network binding, identity authentication and data interaction adaptation of the terminal water purifier devices. S2. Through the multi-dimensional acquisition module mounted on the terminal water purifier, real-time data on equipment operation and water quality conditions are collected. The equipment operation data includes water flow rate, operating temperature, and operating status. The water quality conditions data includes influent TDS value and effluent TDS value. S3. The terminal water purifier uploads the equipment operation data and water quality condition data collected in step S2 to the cloud intelligent management and control platform through an encrypted communication protocol. The cloud intelligent management and control platform cleans, reduces noise, and analyzes the received data in real time. S4. The cloud-based intelligent management and control platform dynamically and adaptively adjusts the pulse detonation frequency and detonation duration based on the equipment operation data and water quality condition data received in step 3. When the TDS value of the incoming water is higher than the preset water turbidity threshold, the cloud-based intelligent management and control platform controls the terminal water purifier to increase the pulse burst frequency and extend the microsecond duration of a single explosion. When the TDS value of the incoming water is lower than the preset water turbidity threshold, the cloud-based intelligent control platform controls the terminal water purifier to reduce the pulse burst frequency and shorten the microsecond duration of a single explosion. The S5 cloud-based intelligent management and control platform monitors abnormal equipment operation data in real time. When abnormal conditions such as excessive water quality, abnormal pressure, equipment jamming, or circuit failure are detected, it actively triggers graded early warnings and issues fault control instructions. S6 The cloud-based intelligent management and control platform receives remote control commands from user terminals. After verifying the compliance of the terminal water purifier's operating status, it enables remote start / stop, mode switching, parameter customization, and timed water production functions for the terminal water purifier. It also synchronously stores user operation data to form a personalized water control profile.

[0006] Specifically, for step S1, the data transmission between the cloud-based intelligent management and control platform and each terminal water purifier is encrypted using the AES encryption algorithm. The cloud-based intelligent management and control platform has a built-in whitelist of device identities, and only terminal water purifiers that have been authenticated and bound can directly access the cloud-based intelligent management and control platform to prevent unauthorized device access and data leakage.

[0007] Specifically, for step S2, the multi-dimensional acquisition module includes a TDS water quality sensor, a flow sensor, a temperature sensor, and an equipment status acquisition unit. Each sensor adopts a fixed high-frequency sampling period to ensure real-time synchronous acquisition of water quality condition data and equipment operation data.

[0008] Specifically, for step S3, data cleaning includes value range threshold cleaning, null value cleaning, and duplicate value cleaning. The value range threshold cleaning is used to remove abnormal data that exceeds the range. The null value cleaning is used to remove null data caused by network jitter. The duplicate value cleaning is used to remove data that is repeatedly uploaded by the same device at the same timestamp.

[0009] Specifically, for step S3, real-time data analysis includes real-time water quality assessment and analysis. This involves comparing the influent TDS value with the standard threshold in real time and substituting it into the calculation formula for the dynamic wastewater ratio R. The influent TDS value is TDS... in The standard threshold is TDS std , Dynamic wastewater ratio R = R O ·[1+μ·(TDS in - TDS std ) / TDS std ]; where R O denoted as the equipment baseline wastewater ratio, and μ as the water quality control correction coefficient.

[0010] Specifically, for step S5, the graded early warning includes a level 1 routine early warning, a level 2 abnormal early warning, and a level 3 fault shutdown early warning. The level 1 routine early warning is used to push routine equipment maintenance reminders. The level 2 abnormal early warning corresponds to slightly excessive water quality and minor abnormalities in operating parameters, and automatically sends fine-tuning and error correction instructions. The level 3 fault shutdown early warning corresponds to serious faults, triggers equipment shutdown protection, and pushes maintenance reminders.

[0011] Specifically, for step S6, the user terminal includes a mobile APP, a mini program, and a smart central control panel, which supports users to customize water purification parameters and timed water production periods.

[0012] Specifically, the cloud-based intelligent management and control platform classifies and persistently stores all equipment operation data, water quality condition data, early warning records, pulse detonation control records, and user operation data, establishing a data archive for the entire life cycle of the equipment, and enabling traceability of equipment operation status, fault source, and maintenance records.

[0013] The beneficial effects of this invention are as follows: I. This invention breaks away from the traditional localized and fixed water production control logic of water purifiers. It innovatively introduces a cloud-based adaptive pulse explosion water production mechanism and dynamically and adaptively adjusts the pulse explosion frequency and duration by collecting real-time TDS water quality data of the incoming water. This completely solves the industry pain points of poor water quality adaptability of traditional equipment, failure to purify water with high turbidity, and serious energy consumption and water waste in low turbidity conditions, and achieves a dynamic balance between water purification effect and water-saving performance.

[0014] Second, this invention constructs a complete and standardized cloud data preprocessing system, which effectively filters out noise data caused by water pump vibration, voltage fluctuation, and water flow impact. It solves the problems of weak data processing capabilities, messy raw data, and frequent false control and false warnings in existing cloud water purification systems, greatly improves the accuracy of cloud data analysis and the reliability of equipment control, and provides accurate data support for intelligent control and fault diagnosis.

[0015] Third, this invention establishes a multi-dimensional real-time data analysis and linkage control system, integrating real-time water quality assessment, dynamic wastewater ratio adaptive correction, equipment operating condition anomaly identification, and user water usage habit time-series analysis. Compared with the shortcomings of traditional equipment that can only upload simple data and lacks in-depth analysis capabilities, this invention adaptively optimizes the water production strategy according to the peak and valley periods of water usage, truly realizing "on-demand water production and intelligent adaptation", significantly improving the overall intelligence level and scenario adaptability of the equipment.

[0016] Fourth, this invention has the advantages of high security, traceability, and cluster management. It achieves full traceability of water quality, faults, and operation and maintenance through persistent archiving of data throughout the entire life cycle. At the same time, it supports remote customized management and control of multiple terminals and cluster management of multiple devices, effectively solving the problems of cumbersome operation and maintenance, single management and control, and high operation and maintenance costs in commercial scenarios of traditional equipment. It is suitable for large-scale intelligent operation and maintenance needs in multiple scenarios such as home, park, and commercial office. Attached Figure Description

[0017] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0019] Combined with appendix Figure 1 As shown, a cloud platform intelligent control method for a water purifier includes the following steps: S1. Establish an IoT communication link between the cloud-based intelligent management and control platform and each terminal water purifier, and complete the network binding, identity authentication and data interaction adaptation of the terminal water purifier devices. The data transmission between the cloud-based intelligent management and control platform and each terminal water purifier is encrypted using the AES encryption algorithm. The cloud-based intelligent management and control platform has a built-in whitelist of device identities, and only terminal water purifiers that have been authenticated and bound can directly access the cloud-based intelligent management and control platform to prevent unauthorized device access and data leakage. This addresses the technical risks of existing cloud-based water purification systems, such as the lack of communication security mechanisms, the susceptibility of raw data transmission to tampering, the chaotic device management caused by unauthorized device access, and the leakage of user water usage data. It ensures the legality of device access and the security of data transmission at the communication link level, and improves the overall system operation stability and privacy. S2. Through the multi-dimensional acquisition module on the terminal water purifier, real-time data on equipment operation and water quality are collected. The equipment operation data includes water flow rate, operating temperature, and operating status. The water quality data includes influent TDS value and effluent TDS value. The multi-dimensional acquisition module includes a TDS water quality sensor, a flow sensor, a temperature sensor, and an equipment status acquisition unit. Each sensor uses a fixed high-frequency sampling period, such as once per second, to ensure real-time synchronous acquisition of water quality data and equipment operation data. This effectively solves the problems of low sampling frequency and delayed data updates of traditional equipment sensors, which cannot capture instantaneous fluctuations in water quality and minor anomalies in equipment operation. It enables high-frequency real-time perception of water quality changes and equipment operation status, avoiding defects such as untimely control and delayed water quality adaptation caused by data lag. To address the shortcomings of existing IoT water purifiers, such as limited data collection dimensions, the ability to upload only a small amount of data, and the inability to fully cover water quality changes and equipment operating conditions, step S2 collects multi-dimensional data synchronously, fully covering core water quality parameters and equipment operating status parameters. This provides a comprehensive and complete data foundation for subsequent cloud-based precise analysis, adaptive parameter control, and fault diagnosis, solving the problems of insufficient data support and one-sided control basis of traditional equipment. S3. The terminal water purifier uploads the equipment operation data and water quality condition data collected in step S2 to the cloud intelligent management and control platform through an encrypted communication protocol. The cloud intelligent management and control platform cleans, reduces noise, and analyzes the received data in real time. To address the industry pain point that existing cloud-based water purification control systems have weak data processing capabilities and that raw data is easily affected by external interference such as water pump vibration, voltage fluctuations, and water flow impact in pipelines, resulting in a large amount of noise data that directly affects the accuracy of equipment control, step S3 sets up a systematic data processing flow to perform standardized preprocessing on raw sensor data, thereby improving data accuracy from the source. Data cleaning includes value range threshold cleaning, null value cleaning, and duplicate value cleaning. Value range threshold cleaning is used to remove out-of-range abnormal data, null value cleaning is used to remove null data caused by network jitter, and duplicate value cleaning is used to remove data uploaded repeatedly by the same device at the same timestamp. This solves the problem that traditional systems cannot identify out-of-range error data, network abnormal null data, and duplicate redundant data, which leads to a large amount of noisy data participating in algorithm calculations, causing parameter misadjustment and false alarms. It achieves preliminary screening and purification of raw data and ensures the legality and validity of input data. For data noise reduction, the cloud-based intelligent management and control platform uses a sliding window mean filtering noise reduction algorithm to smooth the time series data, retain the real working condition change trend, and remove random interference noise. This noise reduction algorithm uses a fixed-length sliding window to calculate the arithmetic mean of continuously collected data points within the window. This mean is then used to replace the original value at the center point of the window. This process is repeated sequentially across all time-series data. This method effectively suppresses high-frequency random noise caused by pump start-up and shutdown vibrations, instantaneous voltage fluctuations in the power grid, and turbulent water flow impacts in pipelines. At the same time, it preserves effective signal characteristics such as slow changes in water quality parameters and gradual trends in equipment operating conditions. This avoids frequent false triggering or over-response of subsequent control commands due to noise interference, significantly improving the signal-to-noise ratio and engineering usability of the original sensor data. For different data types, the algorithm can also dynamically adjust the window width. A larger window is used for gently changing TDS data to enhance the smoothing effect, while a smaller window is used for change-sensitive flow data to retain dynamic response capabilities. This achieves adaptive noise reduction, balancing data stability and real-time requirements. For real-time data analysis, the cloud-based intelligent management and control platform first stores data in layers according to data type, device number, and timestamp. Then, it performs real-time water quality assessment and analysis, equipment operating condition anomaly analysis, and water usage habit time-series analysis. Unlike traditional water purification systems, which suffer from irregular data storage, lack of fine-grained traceability, and single data analysis dimensions, step S3 achieves data traceability, multi-dimensional assessment, and precise control through structured layered storage and multi-dimensional linkage analysis, providing algorithmic support for refined intelligent control. Real-time data analysis includes real-time water quality assessment and analysis. This involves comparing the influent TDS value with the standard threshold in real time, and substituting it into the calculation formula for the dynamic wastewater ratio R to determine in real time whether the water quality meets the standards and trigger the corresponding warning level. The influent TDS value is TDS... in The standard threshold is TDS std , Dynamic wastewater ratio R = R O ·[1+μ·(TDS in - TDS std ) / TDS std ]; where R O The reference wastewater ratio is denoted as μ, which is the water quality control correction coefficient. The value of μ ranges from 0.05 to 0.45, and this range was determined by fitting and calibrating a large number of water purification operating condition experiments and water quality sample data from different regions. A lower limit of μ of 0.05 corresponds to a working condition where the water quality fluctuation is small and the influent TDS is close to the standard threshold. In this case, only a slight correction is needed to meet the water purification requirements, avoiding excessive adjustment that could lead to frequent changes in the wastewater ratio and system oscillation. The upper limit of μ value of 0.45 corresponds to extreme operating conditions where the influent water quality is seriously out of standard and the turbidity changes drastically. At this time, the wastewater ratio needs to be significantly increased to ensure that the concentration polarization on the RO membrane surface is effectively suppressed and the effluent water quality is stable and meets the standards. During operation, the cloud-based intelligent management and control platform continuously and dynamically adjusts the μ value in the range of 0.05 to 0.45 through an online optimization algorithm based on the real-time deviation of the influent TDS value from the standard threshold, combined with the current cumulative operating time of the equipment, the membrane flux decay status and historical control effect feedback, to achieve the optimal matching of the wastewater ratio under different operating conditions. This technology breaks through the limitations of traditional water purifiers, which have a fixed wastewater ratio and cannot adapt to fluctuations in water quality. It dynamically matches the optimal wastewater ratio parameters for influent water of different turbidity levels, ensuring that the effluent water quality meets the standards while solving the dual pain points of incomplete purification of high-turbidity water and waste of water resources in low-turbidity water. Equipment anomaly analysis identifies abnormal operating conditions such as pipeline blockage, water pump failure, and water leakage shutdown based on equipment operation data. Water usage habit time sequence analysis is a statistical analysis of time-sharing flow data to identify peak and off-peak water usage periods in the morning and evening. It automatically learns users' water usage patterns and adapts energy-saving water production and sleep strategies to solve the problem that traditional water purifiers' fixed water production mode cannot adapt to users' water usage time sequence characteristics, resulting in insufficient water supply during peak water usage periods and continuous energy waste during off-peak water usage periods. It achieves personalized intelligent adaptation and control based on user habits. Through the above-mentioned standardized data cleaning, noise reduction, and real-time analysis mechanisms, the problems of messy, interference-prone, and poor-precision sensor data of IoT water purification equipment are thoroughly solved, greatly improving the accuracy of adaptive control and fault early warning, and making the cloud-based intelligent control logic more stable and more in line with actual engineering applications. S4. The cloud-based intelligent management and control platform dynamically and adaptively adjusts the pulse detonation frequency and detonation duration based on the equipment operation data and water quality condition data received in step 3. When the TDS value of the incoming water is higher than the preset water turbidity threshold, the cloud-based intelligent management and control platform controls the terminal water purifier to increase the pulse burst frequency and extend the microsecond duration of a single explosion. When the TDS value of the incoming water is lower than the preset water turbidity threshold, the cloud-based intelligent control platform controls the terminal water purifier to reduce the pulse burst frequency and shorten the microsecond duration of a single explosion. The pulse detonation generating device includes a high-voltage pulse generator and a piezoelectric ceramic detonation actuator. The high-voltage pulse generator receives frequency and duration control signals from the cloud and boosts the DC low voltage into a high-frequency high-voltage pulse signal, which drives the piezoelectric ceramic detonation actuator to generate an instantaneous mechanical shock wave that acts on the RO membrane surface to disrupt the boundary layer polarization effect and increase membrane flux. The control circuit uses an MCU microcontroller as its core, integrating a PWM pulse width modulation drive module, a MOSFET switching array, and an overcurrent and overvoltage protection circuit. The MCU receives control commands transmitted from the cloud platform through a UART or Wi-Fi communication interface, analyzes the target frequency and duty cycle parameters in real time, and outputs the corresponding PWM waveform through a PID closed-loop regulation algorithm to accurately control the pulse output characteristics. The overcurrent and overvoltage protection circuit monitors the operating current and voltage in real time, and automatically cuts off the output to protect the actuator and power supply module when an abnormality is detected. The detonation frequency adjustment range is 1Hz~500Hz, the detonation duration (i.e., the duration of a single pulse) is 50μs~2000μs, the duty cycle is adjustable from 5% to 80%, and the effluent flow rate adjustment accuracy is within ±2%. The above parameters are dynamically calculated and determined by the cloud-based intelligent control platform based on the turbidity of the influent water and the real-time operating conditions, ensuring that the optimal pulse combination can be output under different water quality conditions, achieving synergistic optimization of efficient water purification and energy saving. Compared to the outdated control mode of traditional water purifiers, which features constant-speed continuous water production, fixed parameters, and poor adaptability to operating conditions, this invention innovatively adopts pulse detonation water production control logic. Relying on precise data analysis results from the cloud, it achieves adaptive dynamic control of water quality, solving the problem of water purification adaptability caused by differences in water quality in different regions and at different times. The S5 cloud-based intelligent management and control platform monitors abnormal equipment operation data in real time. When abnormal conditions such as excessive water quality, abnormal pressure, equipment jamming, or circuit failure are detected, it actively triggers graded early warnings and issues fault control instructions. The tiered early warning system includes Level 1 routine early warning, Level 2 abnormal early warning, and Level 3 fault shutdown early warning. Level 1 routine early warning is used to push routine equipment maintenance reminders. Level 2 abnormal early warning corresponds to minor water quality exceedances and slight abnormalities in operating parameters, and automatically issues fine-tuning and error correction instructions. Level 3 fault shutdown early warning corresponds to serious faults, triggering equipment shutdown protection and pushing maintenance reminders. This system constructs a multi-level and differentiated fault protection system, changing the traditional one-size-fits-all alarm and shutdown mode of equipment. It enables autonomous repair of minor abnormalities, timely reminders of moderate abnormalities, and safety protection for severe abnormalities, taking into account both water supply continuity and equipment safety. The S6 cloud-based intelligent management and control platform receives remote control commands from user terminals. After verifying the compliance of the terminal water purifier's operating status, it enables remote start / stop, mode switching, parameter customization, and timed water production functions for the terminal water purifier. It also synchronously stores user operation data to form a personalized water control profile. This breaks through the limitations of traditional water purifiers, which only support local button operation, have a single management method, and lack personalized adaptation capabilities. It leverages the advantages of cloud-based IoT to achieve remote intelligent management and control. At the same time, it builds a personalized water system based on user operation data, improving the user experience and the intelligence level of the equipment. User terminals include mobile apps, mini-programs, and smart control panels, supporting users to customize water purification parameters and timed water production periods. They are adaptable to the needs of multiple scenarios and multiple terminals for convenient operation, solving the problems of single control channels and poor flexibility of traditional smart water purification equipment, and greatly improving the convenience of equipment operation, maintenance and use.

[0020] The cloud-based intelligent management and control platform classifies and persistently stores all equipment operation data, water quality data, early warning records, pulse explosion control records, and user operation data, establishing a full lifecycle data archive for the equipment. This enables traceability of equipment operation status, fault location, and maintenance records, addressing the shortcomings of existing cloud-based water purification systems such as fragmented data retention, lack of systematic archiving, and inability to achieve maintenance traceability. It meets the needs of refined maintenance, fault review, and water quality traceability in commercial and industrial park scenarios with multiple equipment, significantly reducing the difficulty and cost of equipment maintenance.

[0021] The above description does not limit the technical scope of the present invention in any way. Any modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the technical scope of the present invention.

Claims

1. A cloud-based intelligent control method for a water purifier, characterized in that: It includes the following steps: S1. Establish an IoT communication link between the cloud-based intelligent management and control platform and each terminal water purifier, and complete the network binding, identity authentication and data interaction adaptation of the terminal water purifier devices. S2. Through the multi-dimensional acquisition module mounted on the terminal water purifier, real-time data on equipment operation and water quality conditions are collected. The equipment operation data includes water flow rate, operating temperature, and operating status. The water quality conditions data includes influent TDS value and effluent TDS value. S3. The terminal water purifier uploads the equipment operation data and water quality condition data collected in step S2 to the cloud intelligent management and control platform through an encrypted communication protocol. The cloud intelligent management and control platform cleans, reduces noise, and analyzes the received data in real time. S4. The cloud-based intelligent management and control platform dynamically and adaptively adjusts the pulse detonation frequency and detonation duration based on the equipment operation data and water quality condition data received in step 3. When the TDS value of the incoming water is higher than the preset water turbidity threshold, the cloud-based intelligent management and control platform controls the terminal water purifier to increase the pulse burst frequency and extend the microsecond duration of a single explosion. When the TDS value of the incoming water is lower than the preset water turbidity threshold, the cloud-based intelligent control platform controls the terminal water purifier to reduce the pulse burst frequency and shorten the microsecond duration of a single explosion. The S5 cloud-based intelligent management and control platform monitors abnormal equipment operation data in real time. When abnormal conditions such as excessive water quality, abnormal pressure, equipment jamming, or circuit failure are detected, it actively triggers graded early warnings and issues fault control instructions. S6 The cloud-based intelligent management and control platform receives remote control commands from user terminals. After verifying the compliance of the terminal water purifier's operating status, it enables remote start / stop, mode switching, parameter customization, and timed water production functions for the terminal water purifier. It also synchronously stores user operation data to form a personalized water control profile.

2. The cloud platform intelligent control method for a water purifier according to claim 1, characterized in that: For step S1, the data transmission between the cloud-based intelligent management and control platform and each terminal water purifier is encrypted using the AES encryption algorithm. The cloud-based intelligent management and control platform has a built-in whitelist of device identities, and only terminal water purifiers that have been authenticated and bound can directly access the cloud-based intelligent management and control platform to prevent unauthorized device access and data leakage.

3. The cloud platform intelligent control method for a water purifier according to claim 1, characterized in that: For step S2, the multi-dimensional acquisition module includes a TDS water quality sensor, a flow sensor, a temperature sensor, and an equipment status acquisition unit. Each sensor adopts a fixed high-frequency sampling period to ensure real-time synchronous acquisition of water quality condition data and equipment operation data.

4. The cloud platform intelligent control method for a water purifier according to claim 1, characterized in that: For step S3, data cleaning includes value range threshold cleaning, null value cleaning, and duplicate value cleaning. The value range threshold cleaning is used to remove abnormal data that exceeds the range. The null value cleaning is used to remove null data caused by network jitter. The duplicate value cleaning is used to remove data that is repeatedly uploaded by the same device at the same timestamp.

5. The cloud platform intelligent control method for a water purifier according to claim 1, characterized in that: For step S3, real-time data analysis includes real-time water quality assessment and analysis. This involves comparing the influent TDS value with the standard threshold in real time and substituting it into the calculation formula for the dynamic wastewater ratio R. The influent TDS value is TDS... in The standard threshold is TDS std , Dynamic wastewater ratio R = R O ·[1+μ·(TDS in - TDS std ) / TDS std ]; where R O denoted as the equipment baseline wastewater ratio, and μ as the water quality control correction coefficient.

6. The cloud platform intelligent control method for a water purifier according to claim 1, characterized in that: For step S5, the graded early warning includes a first-level routine early warning, a second-level abnormal early warning, and a third-level fault shutdown early warning. Level 1 routine warning is used to push routine equipment maintenance reminders; Level 2 abnormal warning corresponds to slight water quality exceeding the standard or minor abnormalities in operating parameters and automatically issues fine-tuning and error correction instructions; Level 3 fault shutdown warning corresponds to serious faults, triggers equipment shutdown protection and pushes maintenance reminders.

7. The cloud platform intelligent control method for a water purifier according to claim 1, characterized in that: For step S6, the user terminal includes a mobile APP, a mini program, and a smart central control panel, which supports users to customize water purification parameters and timed water production periods.

8. The cloud platform intelligent control method for a water purifier according to claim 1, characterized in that: The cloud-based intelligent management and control platform classifies and persistently stores all equipment operation data, water quality data, early warning records, pulse detonation control records, and user operation data, establishing a data archive for the entire equipment lifecycle, and enabling traceability of equipment operation status, fault location, and maintenance records.

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