Water purification equipment control optimization system based on Internet of Things

Abstract

The invention discloses a water purification equipment control optimization system based on the Internet of Things, and relates to the technical field of equipment optimization. The method comprises the following steps: obtaining physical information of the water purification equipment, constructing a physical model, obtaining pre-purification data and post-purification data of the water purification equipment, constructing a simulation model to obtain membrane performance parameters, and obtaining membrane flux, recovery rate and retention rate at the same moment; respectively adjusting the pressure difference, the membrane flux and the recovery rate at the same moment in the simulation model to obtain a blocking coefficient, constructing a blocking evaluation model in combination with membrane performance parameters, judging whether a membrane leakage fault exists or not according to the rejection rate, replacing the membrane leakage fault, obtaining a predicted blocking coefficient by utilizing the blocking evaluation model, and judging whether the membrane leakage fault exists or not. According to the predicted blocking coefficient, whether a membrane blocking fault exists or not is judged, and the membrane blocking fault is flushed; the running condition of the RO membrane element can be reflected from different angles, so that the working time of the RO membrane element is prolonged, and the total cost of water purification work of the water purification equipment is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment optimization, and in particular to a water purification equipment control optimization system based on the Internet of Things. Background Art

[0002] Control optimization of water purification equipment is an innovative solution that integrates technologies such as the Internet of Things, big data, and cloud computing to achieve intelligent and refined control and management of water purification equipment. It breaks the limitations of traditional water purification equipment's isolated operation and significantly improves the operating efficiency, water quality stability, and user experience of water purification equipment through real-time data interaction and analysis.

[0003] The reverse osmosis system plays the main role in water purification in water purification equipment, especially the RO membrane element in the reverse osmosis system, which can filter out most impurities and pollutants in raw water. The existing technology often emphasizes the overall optimization of the water purification equipment, but ignores the detailed optimization of important components. It fails to analyze and predict the operating status and potential blockage of the RO membrane element based on data from different angles, so that its working life cannot be effectively extended, and the overall water purification cost is increased. In response to the shortcomings of the existing technology, the present invention provides a water purification equipment control optimization system based on the Internet of Things. Summary of the Invention

[0004] The purpose of the present invention is to provide a water purification equipment control optimization system based on the Internet of Things.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A water purification equipment control optimization system based on the Internet of Things, including the following modules:

[0006] The data acquisition module is used to obtain the physical information of the water purification equipment and build a physical model, obtain the pre-purification data and post-purification data of the water purification equipment, and build a simulation model to obtain membrane performance parameters;

[0007] The data analysis module is used to obtain the membrane flux, recovery rate, and retention rate at the same time based on the pre-purification data and post-purification data of the water purification equipment;

[0008] The data simulation module is used to adjust the pressure difference, membrane flux, and recovery rate at the same time in the simulation model and obtain the corresponding blockage coefficient;

[0009] The data evaluation module is used to build a blockage evaluation model for water purification equipment based on the pressure difference, membrane flux, recovery rate, and blockage coefficient at different times and combined with membrane performance parameters;

[0010] The data optimization module is used to determine whether there is a membrane leakage fault based on the retention rate and replace it, use the blockage assessment model to obtain the predicted blockage coefficient, and determine whether there is a membrane blockage fault based on the predicted blockage coefficient and flush it.

[0011] Furthermore, the process of obtaining the physical information of the water purification equipment and building a physical model includes:

[0012] The water purification equipment refers to equipment that converts raw water into purified water that meets the requirements of a specific use, and the physical information refers to the physical structure and dimensions of each component of the reverse osmosis system in the water purification equipment;

[0013] The reverse osmosis system includes a high-pressure pump, an RO membrane element, a membrane shell container, an online conductivity meter, and a flushing device. A corresponding physical model is constructed based on the acquired physical information using a three-dimensional modeling tool.

[0014] Furthermore, the process of obtaining pre-cleaning data and post-cleaning data of the water purification equipment and constructing a simulation model to obtain membrane performance parameters includes:

[0015] The pre-net data refers to the parameter values ​​corresponding to the water quality parameters, pollutant parameters, inlet flow rate, temperature and pressure of the raw water before being treated by the RO membrane element in the reverse osmosis system;

[0016] Using simulation software to simulate the working process of the reverse osmosis system based on its physical model to obtain a corresponding simulation model, uploading the obtained pre-net data to the simulation model for synchronization, and adjusting the membrane characteristic parameters of the RO membrane element in the simulation model;

[0017] The membrane characteristic parameters include membrane permeability, solute permeability, and membrane surface roughness. The simulated post-net data in the simulation model under different values ​​of the membrane characteristic parameters are obtained. When the obtained simulated post-net data is the same as the post-net data at the corresponding moment of the synchronized pre-net data, the membrane characteristic parameters with the corresponding values ​​in the simulation model at this time are used as the membrane performance parameters of the water purification equipment.

[0018] Furthermore, the process of obtaining the membrane flux, recovery rate, and rejection rate at the same time based on the pre-cleaning data and post-cleaning data of the water purification equipment includes:

[0019] Set the analysis cycle. When an analysis cycle is reached, the corresponding membrane flux J is obtained based on the net pre-data and net post-data at the corresponding moment. w and recovery rate R e ;

[0020]

[0021] Q p is the water flow rate in the net data, A is the effective area of ​​RO membrane element, Qf is the inlet flow rate in the pre-net data;

[0022] The retention rate refers to the changes in water quality parameters and pollutant parameters before and after the RO membrane element. The parameter values ​​of the single parameter in the data before and after the net are recorded as C f and C p , obtain the retention rate R of the single parameter at the corresponding time j ;

[0023]

[0024] Obtain the retention rates of other parameters in water quality parameters and pollutant parameters respectively.

[0025] Furthermore, in the simulation model, the pressure difference, membrane flux, and recovery rate at the same time are adjusted respectively, and the corresponding blocking coefficient is obtained. The process includes:

[0026] In the simulation model, various parameters in the pre-net data are adjusted to obtain simulated pre-net data and corresponding simulated post-net data, and the corresponding membrane flux and recovery rate are obtained according to the simulated pre-net data and simulated post-net data at the same time;

[0027] According to the simulated net data and its membrane flux and recovery rate at the same time, the blocking coefficient S of the RO membrane element at the corresponding time is obtained. z ;

[0028]

[0029] P n is the pressure difference in the simulated net data at the corresponding time, J n 、R n are the membrane flux and recovery rate at the corresponding moment, P0, J0, and R0 are the preset initial pressure difference, initial membrane flux, and initial recovery rate;

[0030] In the simulation model, the pressure difference, membrane flux, recovery rate and blockage coefficient at different times are obtained.

[0031] Furthermore, the process of constructing a blockage assessment model for a water purification device based on the pressure difference, membrane flux, recovery rate, and blockage coefficient at different times and combining membrane performance parameters includes:

[0032] Based on the pressure difference, membrane flux, recovery rate at different moments obtained in the simulation model and the corresponding blockage coefficient at the next moment, combined with the membrane performance parameters of the water purification equipment in the simulation model, a blockage evaluation set is generated and divided into a training set and a test set;

[0033] Construct a convolutional neural network, using the pressure difference, membrane flux, recovery rate, and membrane performance parameters at different times in the training set as the input data of the convolutional neural network, and the blockage coefficient corresponding to the next time in the training set as the output data of the convolutional neural network;

[0034] The convolutional neural network is trained to obtain an initial convolutional neural network, and the initial convolutional neural network is verified using a test set. The initial convolutional neural network with an output value less than or equal to a preset test error threshold is used as a blockage assessment model for water purification equipment.

[0035] Furthermore, the process of determining whether there is a membrane leakage fault based on the retention rate and replacing it includes:

[0036] Set a retention rate threshold, and compare the current retention rates of various water quality parameters and pollutant parameters with the set retention rate thresholds. When the retention rate of at least one parameter is greater than the retention rate threshold, it is determined that there is a membrane leakage fault in the RO membrane element of the water purification equipment, and a leakage alarm signal is generated and fed back to the relevant personnel to prompt them to replace the RO membrane element.

[0037] Furthermore, the process of obtaining a predicted blocking coefficient using the blocking assessment model and determining whether a membrane blocking fault exists and flushing the membrane according to the predicted blocking coefficient includes:

[0038] Input the current pressure difference, membrane flux, recovery rate and membrane performance parameters of the water purification equipment into the blockage assessment model to obtain the corresponding predicted blockage coefficient Sy, and set the blockage coefficient range [Smin, Smax];

[0039] Compare the obtained predicted blocking coefficient with the blocking coefficient range. When Sy≤Smin, it is determined that there is no membrane blocking fault in the RO membrane element of the water purification equipment, and no other operation is performed on it.

[0040] When Smin<Sy<Smax, it is judged that the RO membrane element of the water purification equipment has a slight membrane blocking fault, and the flushing device is controlled to flush the RO membrane element with water. When Sy≥Smax, it is judged that the RO membrane element of the water purification equipment has a serious membrane blocking fault, and the flushing device is controlled to perform chemical flushing on the RO membrane element.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention constructs a corresponding simulation model by acquiring different data of the reverse osmosis system in the water purification equipment before and after water purification, and uses the corresponding membrane characteristic parameters in the simulation model as the membrane performance parameters of the current water purification equipment. This is conducive to obtaining the real-time performance of the RO membrane element in the current water purification equipment. By incorporating this into the basis of subsequent evaluation, the accuracy of data evaluation can be improved.

[0043] By obtaining the membrane flux, recovery rate, and rejection rate of the water purification equipment at the same time, the operating status of its RO membrane elements can be reflected from different angles, and the blocking coefficient for comprehensive evaluation of the blocking situation of the RO membrane elements can be obtained. A blocking evaluation model for the water purification equipment can be constructed based on the corresponding relationship between various parameters at different times. The predicted blocking coefficient for the next analysis cycle can be obtained based on the current parameters, providing an effective implementation method for predicting the blocking situation of the RO membrane elements.

[0044] By setting the rejection rate threshold and the blockage coefficient range, the possible fault conditions of the RO membrane elements are divided into leakage and blockage, and the elements can be replaced or cleaned. This helps to extend the working time of the RO membrane elements and reduce the total cost of water purification equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a module schematic diagram of the present invention. DETAILED DESCRIPTION

[0046] like Figure 1 As shown, a water purification equipment control optimization system based on the Internet of Things includes the following modules:

[0047] The data acquisition module is used to obtain the physical information of the water purification equipment and build a physical model, obtain the pre-purification data and post-purification data of the water purification equipment, and build a simulation model to obtain membrane performance parameters;

[0048] The data analysis module is used to obtain the membrane flux, recovery rate, and retention rate at the same time based on the pre-purification data and post-purification data of the water purification equipment;

[0049] The data simulation module is used to adjust the pressure difference, membrane flux, and recovery rate at the same time in the simulation model and obtain the corresponding blockage coefficient;

[0050] The data evaluation module is used to build a blockage evaluation model for water purification equipment based on the pressure difference, membrane flux, recovery rate, and blockage coefficient at different times and combined with membrane performance parameters;

[0051] The data optimization module is used to determine whether there is a membrane leakage fault based on the retention rate and replace it, use the blockage assessment model to obtain the predicted blockage coefficient, and determine whether there is a membrane blockage fault based on the predicted blockage coefficient and flush it.

[0052] It should be further explained that, in the specific implementation process, the process of obtaining the physical information of the water purification equipment and building the physical model includes:

[0053] The water purification equipment refers to equipment that converts raw water into purified water that meets the requirements of specific uses. It mainly includes three process links: pretreatment, core treatment, and post-treatment. The purpose of this invention is to optimize the control of the core treatment link, so only the equipment corresponding to the core treatment link is discussed;

[0054] The core treatment is used to remove soluble salts, ions, organic matter, microorganisms, viruses, etc. in water to achieve high-purity water requirements. It is specifically achieved through a reverse osmosis (RO) system, which includes a high-pressure pump, RO membrane elements, membrane shell containers, online conductivity meters, and flushing devices.

[0055] The high-pressure pump is used to provide the required high pressure for the reverse osmosis system. The RO membrane element is a key filtration unit and is generally a spiral membrane. The membrane shell container is used to load the RO membrane element and withstand high pressure. The online conductivity meter is used to monitor the conductivity of the produced water in real time. The flushing device is used to regularly flush or chemically clean the RO membrane element to restore membrane performance.

[0056] The physical information refers to the various relevant data required to construct the physical model of the core processing link of the water purification equipment, specifically the physical structure dimensions of each component of the reverse osmosis system. The corresponding physical model is constructed based on the acquired physical information using a three-dimensional modeling tool.

[0057] It should be further explained that, in the specific implementation process, the process of obtaining the pre-cleaning data and post-cleaning data of the water purification equipment and constructing a simulation model to obtain the membrane performance parameters includes:

[0058] The pre-net data refers to the parameter values ​​corresponding to the water quality parameters, pollutant parameters, inlet flow rate, temperature and pressure of the raw water before being treated by the RO membrane element in the reverse osmosis system;

[0059] The water quality parameters include TDS, Ca 2+ Mg 2+ 、SO4 2- 、HCO3 - , SiO2, the pollutant parameters include TOC, turbidity, SDI 15 , microbial ATP value;

[0060] The post-purification data refers to the parameter values ​​corresponding to the water quality parameters, pollutant parameters, water production flow rate, conductivity, and pressure difference of the purified water after treatment by the RO membrane element in the reverse osmosis system;

[0061] Using simulation software to simulate the working process of the reverse osmosis system based on its physical model to obtain a corresponding simulation model, uploading the obtained pre-net data to the simulation model for synchronization, and adjusting the membrane characteristic parameters of the RO membrane element in the simulation model;

[0062] The membrane characteristic parameters include membrane permeability, solute permeability, and membrane surface roughness. The simulated post-net data in the simulation model under different values ​​of the membrane characteristic parameters are obtained. When the obtained simulated post-net data is the same as the post-net data at the corresponding moment of the synchronized pre-net data, the membrane characteristic parameters with the corresponding values ​​in the simulation model at this time are used as the membrane performance parameters of the water purification equipment.

[0063] It should be further explained that, in the specific implementation process, the process of obtaining the membrane flux, recovery rate, and retention rate at the same time based on the pre-cleaning data and post-cleaning data of the water purification equipment includes:

[0064] Set the analysis cycle. When an analysis cycle is reached, the corresponding membrane flux and recovery rate are obtained based on the pre-net data and post-net data at the corresponding moment, which are recorded as J and J respectively. w and R e ;

[0065]

[0066] Among them, Q p is the water flow rate in the net data, A is the effective area of ​​RO membrane element, Q f is the inlet flow rate in the pre-net data;

[0067] The retention rate refers to the changes in various water quality parameters and pollutant parameters before and after the RO membrane element. Taking a single parameter as an example, its parameter value in the pre-net data is recorded as C f , and its parameter value in the net data is recorded as C p , obtain the interception rate of the single parameter at the corresponding time, recorded as R j ;

[0068]

[0069] The same method is used to obtain the retention rates of various water quality parameters and pollutant parameters.

[0070] It should be further explained that, in the specific implementation process, the pressure difference, membrane flux, and recovery rate at the same time are adjusted in the simulation model, and the process of obtaining the corresponding blockage coefficient includes:

[0071] In the simulation model, each parameter in the pre-net data is adjusted respectively, and the corresponding simulated post-net data is obtained. The pre-net data under each adjustment is marked as simulated pre-net data, and the corresponding membrane flux and recovery rate are obtained based on the simulated pre-net data and simulated post-net data at the same time;

[0072] According to the simulated net data and its membrane flux and recovery rate at the same time, the blocking coefficient of the RO membrane element at the corresponding time is obtained, which is recorded as S z ;

[0073]

[0074] Among them, P n is the pressure difference in the simulated net data at the corresponding time, J n 、R n are the membrane flux and recovery rate at the corresponding moment, P0, J0, and R0 are the preset initial pressure difference, initial membrane flux, and initial recovery rate;

[0075] The same method is used to obtain the pressure difference, membrane flux, recovery rate and the corresponding blockage coefficient at different times in the simulation model.

[0076] It should be further explained that, in the specific implementation process, the process of constructing a blockage assessment model for water purification equipment based on the pressure difference, membrane flux, recovery rate, and blockage coefficient at different times and combining membrane performance parameters includes:

[0077] Based on the pressure difference, membrane flux, recovery rate at different moments obtained in the simulation model and the corresponding blockage coefficient at the next moment, combined with the membrane performance parameters of the water purification equipment in the simulation model, a blockage evaluation set is generated and divided into a training set and a test set;

[0078] Construct a convolutional neural network, using the pressure difference, membrane flux, recovery rate, and membrane performance parameters at different times in the training set as the input data of the convolutional neural network, and the blockage coefficient corresponding to the next time in the training set as the output data of the convolutional neural network;

[0079] The convolutional neural network is trained to obtain an initial convolutional neural network, and the initial convolutional neural network is verified using a test set. The initial convolutional neural network with an output value less than or equal to a preset test error threshold is used as a blockage assessment model for water purification equipment.

[0080] It should be further explained that, in the specific implementation process, the process of determining whether there is a membrane leakage fault based on the retention rate and replacing it includes:

[0081] Set a retention rate threshold, and compare the current retention rate of various water quality parameters and pollutant parameters with the set retention rate threshold. When the retention rate of at least one parameter is greater than the retention rate threshold, it is determined that the RO membrane element of the water purification equipment has a membrane leakage fault, and a corresponding leakage alarm signal is generated. The generated leakage alarm signal is fed back to the relevant personnel to prompt them to repair and replace the RO membrane element in a timely manner.

[0082] It should be further explained that, in a specific implementation process, the process of obtaining a predicted blockage coefficient using a blockage assessment model, determining whether a membrane blockage fault exists based on the predicted blockage coefficient, and flushing the membrane includes:

[0083] The current pressure difference, membrane flux, recovery rate and membrane performance parameters of the water purification equipment are input into the blockage assessment model, and the corresponding predicted blockage coefficient S is output by the blockage assessment model. y ;

[0084] Set the blocking coefficient range [S min , S max ], compare the obtained predicted blocking coefficient with the blocking coefficient range, when S y ≤S min When the RO membrane element of the water purification equipment is judged to have no membrane blocking fault, no other operations are performed on it;

[0085] When S min <S y <S max When S y ≥S max When the RO membrane element of the water purification equipment is detected to have serious membrane blockage, the flushing device is controlled to perform chemical flushing on the RO membrane element;

[0086] The cleaning includes water cleaning and chemical cleaning, and the membrane blocking failure includes no membrane blocking failure, slight membrane blocking failure, and severe membrane blocking failure.

[0087] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A water purification equipment control optimization system based on the Internet of Things, characterized in that: Includes the following modules: The data acquisition module is used to obtain the physical information of the water purification equipment and build a physical model, obtain the pre-purification data and post-purification data of the water purification equipment, and build a simulation model to obtain membrane performance parameters; The data analysis module is used to obtain the membrane flux, recovery rate, and retention rate at the same time based on the pre-purification data and post-purification data of the water purification equipment; The data simulation module is used to adjust the pressure difference, membrane flux, and recovery rate at the same time in the simulation model and obtain the corresponding blockage coefficient; The data evaluation module is used to build a blockage evaluation model for water purification equipment based on the pressure difference, membrane flux, recovery rate, and blockage coefficient at different times and combined with membrane performance parameters; The data optimization module is used to determine whether there is a membrane leakage fault based on the retention rate and replace it, use the blockage assessment model to obtain the predicted blockage coefficient, and determine whether there is a membrane blockage fault based on the predicted blockage coefficient and flush it.

2. The water purification equipment control optimization system based on the Internet of Things according to claim 1 is characterized in that: The process of obtaining the physical information of the water purification equipment and building a physical model includes: The water purification equipment refers to equipment that converts raw water into purified water that meets the requirements of a specific use, and the physical information refers to the physical structure and dimensions of each component of the reverse osmosis system in the water purification equipment; The reverse osmosis system includes a high-pressure pump, an RO membrane element, a membrane shell container, an online conductivity meter, and a flushing device. A corresponding physical model is constructed based on the acquired physical information using a three-dimensional modeling tool.

3. The water purification equipment control optimization system based on the Internet of Things according to claim 2 is characterized in that: The process of obtaining pre-net data and post-net data and building a simulation model to obtain membrane performance parameters includes: The pre-net data refers to the parameter values ​​corresponding to the water quality parameters, pollutant parameters, inlet flow rate, temperature and pressure of the raw water before treatment by the RO membrane element in the reverse osmosis system; Using simulation software to simulate the working process of the reverse osmosis system based on its physical model to obtain a corresponding simulation model, uploading the obtained pre-net data to the simulation model for synchronization, and adjusting the membrane characteristic parameters of the RO membrane element in the simulation model; The membrane characteristic parameters include membrane permeability, solute permeability, and membrane surface roughness. The simulated post-net data in the simulation model under different values ​​of the membrane characteristic parameters are obtained. When the obtained simulated post-net data is the same as the post-net data at the corresponding moment of the synchronized pre-net data, the membrane characteristic parameters with the corresponding values ​​in the simulation model at this time are used as the membrane performance parameters of the water purification equipment.

4. The water purification equipment control optimization system based on the Internet of Things according to claim 3 is characterized in that: The process of obtaining membrane flux, recovery rate, and rejection rate at the same time includes: Set the analysis cycle. When an analysis cycle is reached, the corresponding membrane flux J is obtained based on the net pre-data and net post-data at the corresponding moment. w and recovery rate R e ; Q p is the water flow rate in the net data, A is the effective area of ​​RO membrane element, Q f is the inlet flow rate in the pre-net data; The retention rate refers to the changes in water quality parameters and pollutant parameters before and after the RO membrane element. The parameter values ​​of the single parameter in the data before and after the net are recorded as C f and C p , obtain the retention rate R of the single parameter at the corresponding time j ; Obtain the retention rates of other parameters in water quality parameters and pollutant parameters respectively.

5. The water purification equipment control optimization system based on the Internet of Things according to claim 4 is characterized in that: The process of adjusting the pressure difference, membrane flux, and recovery rate in the simulation model and obtaining the blockage coefficient includes: In the simulation model, various parameters in the pre-net data are adjusted to obtain simulated pre-net data and corresponding simulated post-net data, and the corresponding membrane flux and recovery rate are obtained according to the simulated pre-net data and simulated post-net data at the same time; According to the simulated net data and its membrane flux and recovery rate at the same time, the blocking coefficient S of the RO membrane element at the corresponding time is obtained. z ; P n is the pressure difference in the simulated net data at the corresponding time, J n 、R n are the membrane flux and recovery rate at the corresponding moment, P0, J0, and R0 are the preset initial pressure difference, initial membrane flux, and initial recovery rate; In the simulation model, the pressure difference, membrane flux, recovery rate and blockage coefficient at different times are obtained.

6. The water purification equipment control optimization system based on the Internet of Things according to claim 5 is characterized in that: The process of building a clogging assessment model for water purification equipment includes: Based on the pressure difference, membrane flux, recovery rate at different moments obtained in the simulation model and the corresponding blockage coefficient at the next moment, combined with the membrane performance parameters of the water purification equipment in the simulation model, a blockage evaluation set is generated and divided into a training set and a test set; Construct a convolutional neural network, using the pressure difference, membrane flux, recovery rate, and membrane performance parameters at different times in the training set as the input data of the convolutional neural network, and the blockage coefficient corresponding to the next time in the training set as the output data of the convolutional neural network; The convolutional neural network is trained to obtain an initial convolutional neural network, and the initial convolutional neural network is verified using a test set. The initial convolutional neural network with an output value less than or equal to a preset test error threshold is used as a blockage assessment model for water purification equipment.

7. The water purification equipment control optimization system based on the Internet of Things according to claim 6 is characterized in that: The process of determining whether there is a membrane leakage fault based on the retention rate and replacing it includes: Set a retention rate threshold, and compare the current retention rates of various water quality parameters and pollutant parameters with the set retention rate thresholds. When the retention rate of at least one parameter is greater than the retention rate threshold, it is determined that there is a membrane leakage fault in the RO membrane element of the water purification equipment, and a leakage alarm signal is generated and fed back to the relevant personnel to prompt them to replace the RO membrane element.

8. The water purification equipment control optimization system based on the Internet of Things according to claim 7 is characterized in that: The process of obtaining the predicted blockage coefficient, determining whether there is a membrane blockage fault, and flushing the membrane blockage fault includes: The current pressure difference, membrane flux, recovery rate and membrane performance parameters of the water purification equipment are input into the blockage assessment model to obtain the corresponding predicted blockage coefficient S y , set the blocking coefficient range [S min , S max ]; Compare the obtained predicted blocking coefficient with the blocking coefficient range. y ≤S min When the RO membrane element of the water purification equipment is judged to have no membrane blocking fault, no other operations are performed on it; When S min <S y <S max When S y ≥S max When the RO membrane element of the water purification equipment is detected, it is judged that there is a serious membrane blockage failure, and the flushing device is controlled to perform chemical flushing on the RO membrane element.

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