A water quality monitoring and intelligent control method, system and medium based on big data

Abstract

The application provides a water quality monitoring and intelligent control method and system based on big data, and a medium. The method comprises the following steps: monitoring a target water treatment device and extracting real-time water quality characteristic data at an inlet, processing the real-time water quality pollution degree coefficient, and determining whether the target water treatment device needs to be started; if the target water treatment device needs to be started, water quality characteristic data corresponding to a first preset time node and a second preset time node at the inlet is obtained, water quality characteristic change rate data is processed, corresponding processing is performed according to physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data, corresponding physical index change rate abnormality coefficient, materialized index change rate abnormality coefficient and biological index change rate abnormality coefficient are obtained, and a reverse osmosis module, an activated carbon filter module and an ultraviolet disinfection module of the target water treatment device are respectively subjected to start determination response, so that the water quality monitoring and intelligent control technology based on big data is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water quality monitoring, in particular to a water quality monitoring and intelligent control method and system based on big data and a medium. BACKGROUND

[0002] With the acceleration of industrialization and population growth, water pollution problems are becoming increasingly serious, traditional water quality monitoring methods have problems such as long monitoring period, inaccurate data, and inability to monitor in real time, which cannot meet the needs of modern water resource management, and traditional water quality control methods also have problems such as inaccurate control and low efficiency.

[0003] Therefore, a new water quality monitoring and intelligent control method is needed to improve the accuracy and real-time performance of water quality monitoring and achieve intelligent control of water quality.

[0004] In view of the above problems, effective technical solutions are currently needed. SUMMARY

[0005] The purpose of the present application is to provide a water quality monitoring and intelligent control method and system based on big data, which can determine whether to start the target water treatment equipment according to the real-time water pollution degree coefficient of the water inlet of the target water treatment equipment, and if it needs to be started, the reverse osmosis module, activated carbon filter module and ultraviolet disinfection module of the target water treatment equipment are respectively started according to the obtained physical index change rate abnormal coefficient, physical and chemical index change rate abnormal coefficient and biological index change rate abnormal coefficient. Abnormal coefficient response, realizing the technology of water quality monitoring and intelligent control based on big data.

[0006] The present application also provides a water quality monitoring and intelligent control method based on big data, comprising the following steps:

[0007] Monitoring the water quality composition information at the water inlet of the target water treatment equipment and extracting real-time water quality characteristic data;

[0008] According to the real-time water quality characteristic data, the real-time water pollution degree coefficient is obtained, and it is judged whether the target water treatment equipment needs to be started;

[0009] If the target water treatment equipment needs to be started, the water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet is obtained, and the corresponding water quality characteristic change rate data is obtained by processing, including physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data;

[0010] According to the physical characteristic change rate data, the physical index change rate abnormal coefficient is obtained, and the reverse osmosis module starting judgment response is carried out;

[0011] According to the physical characteristic change rate data and the chemical characteristic change rate data, a physicochemical index change rate abnormal coefficient is obtained, and a start determination response of the activated carbon filtration module is performed;

[0012] According to the biological characteristic change rate data, a biological index change rate abnormal coefficient is obtained, and a start determination response of the ultraviolet disinfection module is performed.

[0013] Optionally, in the water quality monitoring and intelligent control method based on big data, the water quality component information at the water inlet of the target water treatment equipment is monitored, and real-time water quality characteristic data is extracted, including:

[0014] The water quality component information at the water inlet of the target water treatment equipment is monitored in real time, including physical component information, chemical component information and biological component information;

[0015] The real-time water quality characteristic data is extracted according to the physical component information, the chemical component information and the biological component information, including real-time physical characteristic data, real-time chemical characteristic data and real-time biological characteristic data;

[0016] The real-time physical characteristic data includes real-time salt content data, real-time hardness data, real-time heavy metal content data, real-time colority data and real-time odor intensity data;

[0017] The real-time chemical characteristic data includes real-time chemical oxygen demand data and real-time biochemical oxygen demand data;

[0018] The real-time biological characteristic data includes real-time total bacterial count data and real-time total coliform count data.

[0019] Optionally, in the water quality monitoring and intelligent control method based on big data, the real-time water quality characteristic data is processed to obtain a real-time water quality pollution degree coefficient, and it is determined whether the target water treatment equipment needs to be started, including:

[0020] The real-time water quality characteristic data is processed by a preset water quality pollution degree detection model to obtain a real-time water quality pollution degree coefficient;

[0021] The real-time water quality pollution degree coefficient is compared with a preset water quality pollution degree threshold to obtain a comparison result;

[0022] If the real-time water quality pollution degree coefficient is greater than the preset water quality pollution degree threshold, the target water treatment equipment is started.

[0023] Optionally, in the big data-based water quality monitoring and intelligent control method described in this application, if it is necessary to start the target water treatment equipment, the step of acquiring water quality characteristic data corresponding to the first preset time node and the second preset time node at the inlet, and processing to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data, and biological characteristic change rate data, includes:

[0024] If it is necessary to start the target water treatment equipment, obtain the water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet;

[0025] Based on the water quality characteristic data corresponding to the first preset time node and the water quality characteristic data corresponding to the second preset time node, corresponding processing is performed to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data.

[0026] The physical characteristic change rate data includes salt content change rate data, hardness change rate data, heavy metal content change rate data, color change rate data, and odor intensity change rate data.

[0027] The chemical characteristic change rate data includes chemical oxygen demand change rate data and biochemical oxygen demand change rate data;

[0028] The biometric change rate data includes the total bacterial count change rate data and the total coliform count change rate data.

[0029] Optionally, in the water quality monitoring and intelligent control method based on big data described in this application, the step of processing the physical characteristic change rate data to obtain the physical index change rate anomaly coefficient and performing a reverse osmosis module start-up judgment response includes:

[0030] The physical index change rate anomaly coefficient is obtained by processing the data on salt content change rate, hardness change rate, and heavy metal content change rate.

[0031] The comparison results are obtained by comparing the abnormal coefficient of the rate of change of the physical index with the preset abnormal threshold of the rate of change of the physical index.

[0032] If the abnormal coefficient of the change rate of the physical index is greater than the preset abnormal threshold of the change rate of the physical index, then the reverse osmosis module of the target water treatment equipment is started, and the reverse osmosis operation parameters of the reverse osmosis module are adjusted accordingly.

[0033] The reverse osmosis operating parameters include feed water parameters, product water parameters, and membrane performance parameters.

[0034] Optionally, in the water quality monitoring and intelligent control method based on big data described in this application, the step of processing the physical characteristic change rate data and chemical characteristic change rate data to obtain the physicochemical index change rate anomaly coefficient, and performing an activated carbon filtration module activation judgment response, includes:

[0035] Based on the color change rate data and odor intensity change rate data, combined with the chemical oxygen demand change rate data and biochemical oxygen demand change rate data, the abnormal coefficient of physicochemical index change rate is obtained.

[0036] The comparison results are obtained by comparing the abnormal coefficient of the change rate of the physical and chemical indicators with the preset abnormal threshold of the change rate of the physical and chemical indicators.

[0037] If the abnormal coefficient of the change rate of the physicochemical index is greater than the preset abnormal threshold of the change rate of the physicochemical index, the activated carbon filtration module of the target water treatment equipment is activated, and the filtration operation parameters of the activated carbon filtration module are adjusted accordingly.

[0038] The filtration operation parameters include activated carbon characteristic parameters and filtration system parameters.

[0039] Secondly, this application provides a water quality monitoring and intelligent control system based on big data. The system includes a memory and a processor. The memory includes a program for a water quality monitoring and intelligent control method based on big data. When the program for the water quality monitoring and intelligent control method based on big data is executed by the processor, it performs the following steps:

[0040] Monitor the water composition information at the inlet of the target water treatment equipment and extract real-time water quality characteristic data;

[0041] The real-time water quality characteristic data is processed to obtain the real-time water pollution coefficient, and it is determined whether the target water treatment equipment needs to be started.

[0042] If the target water treatment equipment needs to be started, the water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet are obtained, and the corresponding water quality characteristic change rate data are processed to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data.

[0043] The physical characteristic change rate data is processed to obtain the physical index change rate anomaly coefficient, and a reverse osmosis module start-up judgment response is performed.

[0044] The physical characteristic change rate data and chemical characteristic change rate data are processed to obtain the abnormal coefficient of the physicochemical index change rate, and the activated carbon filtration module start-up judgment response is performed.

[0045] The biological characteristic change rate data is processed to obtain the abnormal coefficient of the biological indicator change rate, and the ultraviolet disinfection module is activated and determined.

[0046] Optionally, in the big data-based water quality monitoring and intelligent control system described in this application, the monitoring of water composition information at the inlet of the target water treatment equipment and the extraction of real-time water quality characteristic data include:

[0047] Real-time monitoring of water composition information at the inlet of the target water treatment equipment, including physical composition information, chemical composition information and biological composition information;

[0048] Real-time water quality characteristic data, including real-time physical characteristic data, real-time chemical characteristic data, and real-time biological characteristic data, are extracted based on the physical component information, chemical component information, and biological component information.

[0049] The real-time physical characteristic data includes real-time salinity data, real-time hardness data, real-time heavy metal content data, real-time color data, and real-time odor intensity data.

[0050] The real-time chemical characteristic data includes real-time chemical oxygen demand data and real-time biochemical oxygen demand data;

[0051] The real-time biometric data includes real-time total bacterial count data and real-time total coliform count data.

[0052] Optionally, in the big data-based water quality monitoring and intelligent control system described in this application, the step of processing the real-time water quality characteristic data to obtain the real-time water pollution coefficient and determining whether the target water treatment equipment needs to be activated includes:

[0053] The real-time water quality characteristic data is processed by a preset water pollution degree detection model to obtain the real-time water pollution degree coefficient.

[0054] The comparison results are obtained by comparing the real-time water pollution coefficient with the preset water pollution threshold.

[0055] If the real-time water pollution coefficient is greater than the preset water pollution threshold, the target water treatment equipment will be activated.

[0056] Thirdly, this application also provides a computer-readable storage medium storing a water quality monitoring and intelligent control method program based on big data. When the big data-based water quality monitoring and intelligent control method program is executed by a processor, it implements the steps of the big data-based water quality monitoring and intelligent control method as described in any of the above claims.

[0057] As can be seen from the above, the water quality monitoring and intelligent control method, system, and medium based on big data provided in this application monitor the water composition information at the inlet of the target water treatment equipment and extract real-time water quality characteristic data, including real-time physical characteristic data, real-time chemical characteristic data, and real-time biological characteristic data. Based on the real-time water quality characteristic data, a real-time water pollution coefficient is obtained, and it is determined whether the target water treatment equipment needs to be activated. If the target water treatment equipment needs to be activated, the water quality characteristic data corresponding to the first and second preset time nodes at the inlet are obtained and processed to obtain the corresponding water quality characteristic change rate data, including the physical characteristic change rate. Based on the data of changes in physical, chemical, and biological characteristics, and by processing the data of changes in physical characteristics, anomaly coefficients of changes in physical indicators are obtained. This data is then used to determine the activation of the reverse osmosis module of the target water treatment equipment. Similarly, by processing the data of changes in physical and chemical characteristics, anomaly coefficients of changes in physicochemical indicators are obtained, and the activation of the activated carbon filtration module of the target water treatment equipment is determined. Finally, by processing the data of changes in biological characteristics, anomaly coefficients of changes in biological indicators are obtained, and the activation of the ultraviolet disinfection module of the target water treatment equipment is determined. This enables water quality monitoring and intelligent control technology based on big data.

[0058] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 A flowchart of a water quality monitoring and intelligent control method based on big data provided in this application embodiment;

[0061] Figure 2 A flowchart illustrating the extraction of real-time water quality characteristic data in the big data-based water quality monitoring and intelligent control method provided in this application embodiment;

[0062] Figure 3 A flowchart illustrating the determination of whether the target water treatment equipment needs to be started using the big data-based water quality monitoring and intelligent control method provided in this application embodiment;

[0063] Figure 4 This is a flowchart illustrating the process of obtaining corresponding water quality characteristic change rate data using a big data-based water quality monitoring and intelligent control method provided in this application embodiment. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0065] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0066] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a water quality monitoring and intelligent control method based on big data according to some embodiments of this application. This big data-based water quality monitoring and intelligent control method is used in terminal devices, such as computers and mobile terminals. The big data-based water quality monitoring and intelligent control method includes the following steps:

[0067] S11. Monitor the water quality composition information at the inlet of the target water treatment equipment and extract real-time water quality characteristic data;

[0068] S12. Process the real-time water quality characteristic data to obtain the real-time water pollution coefficient, and determine whether the target water treatment equipment needs to be started.

[0069] S13. If it is necessary to start the target water treatment equipment, obtain the water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet, and process them to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data.

[0070] S14. Process the physical characteristic change rate data to obtain the physical index change rate anomaly coefficient, and perform a reverse osmosis module start-up judgment response.

[0071] S15. Process the physical characteristic change rate data and chemical characteristic change rate data to obtain the abnormal coefficient of physicochemical index change rate, and perform an activated carbon filtration module start-up judgment response.

[0072] S16. Process the biometric change rate data to obtain the biometric change rate anomaly coefficient, and initiate the ultraviolet disinfection module activation judgment response.

[0073] It should be noted that with the acceleration of industrialization and population growth, water pollution is becoming increasingly serious. Traditional water quality monitoring methods suffer from problems such as long monitoring cycles, inaccurate data, and inability to monitor in real time, failing to meet the needs of modern water resource management. Simultaneously, traditional water quality control methods also suffer from imprecise control and low efficiency. Therefore, a new water quality monitoring and intelligent control method is needed to improve the accuracy and real-time performance of water quality monitoring and achieve intelligent water quality control. In this embodiment, the water composition information at the inlet of the target water treatment equipment is first monitored, and real-time water quality characteristic data, including real-time physical characteristic data, real-time chemical characteristic data, and real-time biological characteristic data, is extracted. Based on the real-time water quality characteristic data, a real-time water pollution coefficient is obtained, and it is determined whether the target water treatment equipment needs to be activated. If activation is required, the target water treatment equipment is activated. The processing equipment acquires water quality characteristic data at the first and second preset time nodes at the inlet, and processes the data to obtain corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data, and biological characteristic change rate data. Based on the physical characteristic change rate data, it obtains the physical index change rate anomaly coefficient and initiates a start-up judgment response for the reverse osmosis module of the target water treatment equipment. Based on the physical and chemical characteristic change rate data, it obtains the physicochemical index change rate anomaly coefficient and initiates a start-up judgment response for the activated carbon filter module of the target water treatment equipment. Based on the biological characteristic change rate data, it obtains the biological index change rate anomaly coefficient and initiates a start-up judgment response for the ultraviolet disinfection module of the target water treatment equipment, thereby realizing a water quality monitoring and intelligent control technology based on big data.

[0074] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the extraction of real-time water quality characteristic data in a water quality monitoring and intelligent control method based on big data, as described in some embodiments of this application. According to embodiments of the present invention, monitoring the water composition information at the inlet of the target water treatment equipment and extracting real-time water quality characteristic data includes:

[0075] S21. Real-time monitoring of water composition information at the inlet of the target water treatment equipment, including physical composition information, chemical composition information and biological composition information;

[0076] S22. Extract real-time water quality characteristic data based on the physical component information, chemical component information and biological component information, including real-time physical characteristic data, real-time chemical characteristic data and real-time biological characteristic data;

[0077] S23. The real-time physical characteristic data includes real-time salinity data, real-time hardness data, real-time heavy metal content data, real-time color data, and real-time odor intensity data.

[0078] S24. The real-time chemical characteristic data includes real-time chemical oxygen demand data and real-time biochemical oxygen demand data;

[0079] S25. The real-time biometric data includes real-time total bacterial count data and real-time total coliform count data.

[0080] It should be noted that before monitoring water quality, it is necessary to understand the composition of the water as much as possible. Therefore, it is necessary to monitor the water composition information at the inlet of the target water treatment equipment in real time, including physical, chemical and biological components. Based on the above information, real-time physical characteristic data, real-time chemical characteristic data and real-time biological characteristic data of the water quality should be extracted. Among them, the real-time physical characteristic data includes real-time salinity, real-time hardness, real-time heavy metal content, real-time color and real-time odor intensity data; the real-time chemical characteristic data includes real-time chemical oxygen demand and real-time biochemical oxygen demand data; and the real-time biological characteristic data includes real-time total bacteria count and real-time total coliform count data.

[0081] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the determination of whether to activate the target water treatment equipment using a big data-based water quality monitoring and intelligent control method in some embodiments of this application. According to an embodiment of the present invention, the step of processing the real-time water quality characteristic data to obtain a real-time water pollution coefficient and determining whether to activate the target water treatment equipment includes:

[0082] S31. The real-time water quality characteristic data is processed by a preset water pollution degree detection model to obtain the real-time water pollution degree coefficient.

[0083] S32. Compare the real-time water pollution coefficient with the preset water pollution threshold to obtain the comparison result;

[0084] S33. If the real-time water pollution coefficient is greater than the preset water pollution threshold, then the target water treatment equipment is started.

[0085] It should be noted that the degree of water pollution needs to be assessed. When the water quality reaches a certain level of pollution, the target water treatment equipment needs to be activated to purify the water. Therefore, real-time water quality characteristic data is processed through a preset water pollution detection model to obtain a real-time water pollution coefficient. The water pollution detection model is a neural network model, which is trained on an initial water pollution detection model based on a large amount of historical water quality characteristic data to obtain a trained water pollution detection model. The model is then used to determine whether the coefficient exceeds a preset value. If it does, the target water treatment equipment needs to be activated.

[0086] Please refer to Figure 4 , Figure 4 This is a flowchart illustrating the process of obtaining corresponding water quality characteristic change rate data in some embodiments of the water quality monitoring and intelligent control method based on big data in this application. According to an embodiment of the present invention, if it is necessary to start the target water treatment equipment, the water quality characteristic data corresponding to the first preset time node and the second preset time node at the inlet are acquired, and the corresponding water quality characteristic change rate data are processed to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data, and biological characteristic change rate data, including:

[0087] S41. If it is necessary to start the target water treatment equipment, obtain the water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet.

[0088] S42. Based on the water quality characteristic data corresponding to the first preset time node and the water quality characteristic data corresponding to the second preset time node, respectively, corresponding processing is performed to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data.

[0089] S43. The physical characteristic change rate data includes salt content change rate data, hardness change rate data, heavy metal content change rate data, color change rate data, and odor intensity change rate data.

[0090] S44. The chemical characteristic change rate data includes chemical oxygen demand change rate data and biochemical oxygen demand change rate data;

[0091] S45. The biometric change rate data includes the total bacterial count change rate data and the total coliform count change rate data.

[0092] It should be noted that if it is determined that the target water treatment equipment needs to be activated for water purification, it is necessary to further understand the changes in water pollution indicators. If the corresponding water quality indicators gradually worsen over time, it indicates normal pollution, and the water purification work can be completed by operating the equipment according to the original conventional operating parameters. However, if the changes in water pollution indicators are rapid and sudden, it indicates that the water quality is affected by other pollution sources and the pollution is unconventional. Therefore, it is necessary to adjust the parameters of the corresponding treatment modules of the target water treatment equipment. The treatment modules of the target water treatment equipment include a reverse osmosis module, an activated carbon filtration module, and an ultraviolet disinfection module. Among them, the water quality characteristic change rate data includes physical characteristic change rate, chemical characteristic change rate, and biological characteristic change rate data. The physical characteristic change rate data includes the change rate of salinity, hardness, heavy metal content, color, and odor intensity. The chemical characteristic change rate data includes the change rate of chemical oxygen demand and biochemical oxygen demand. The biological characteristic change rate data includes the change rate of total bacteria count and total coliform count.

[0093] According to an embodiment of the present invention, the step of processing the physical characteristic change rate data to obtain the physical index change rate anomaly coefficient and performing a reverse osmosis module start-up determination response includes:

[0094] The physical index change rate anomaly coefficient is obtained by processing the data on salt content change rate, hardness change rate, and heavy metal content change rate.

[0095] The comparison results are obtained by comparing the abnormal coefficient of the rate of change of the physical index with the preset abnormal threshold of the rate of change of the physical index.

[0096] If the abnormal coefficient of the change rate of the physical index is greater than the preset abnormal threshold of the change rate of the physical index, then the reverse osmosis module of the target water treatment equipment is started, and the reverse osmosis operation parameters of the reverse osmosis module are adjusted accordingly.

[0097] The reverse osmosis operating parameters include feed water parameters, product water parameters, and membrane performance parameters.

[0098] It should be noted that the data on the rate of change of salinity, hardness and heavy metal content of water are processed to obtain the abnormal coefficient of the rate of change of physical indicators of water quality. If the abnormal amount of the rate of change of physical indicators of water quality exceeds the preset threshold, the reverse osmosis module's reverse osmosis operating parameters are adjusted and the reverse osmosis module is started. The reverse osmosis operating parameters include feed water parameters, product water parameters and membrane performance parameters.

[0099] The formula for calculating the anomaly coefficient of the rate of change of the physical index is as follows:

[0100] ;

[0101] in, This refers to the anomaly coefficient of the rate of change of physical indicators. , , These are data on the rate of change in salt content, the rate of change in hardness, and the rate of change in heavy metal content, respectively. The preset correction coefficient can be determined through experimental fitting or theoretical derivation based on the actual needs of the scenario.

[0102] According to an embodiment of the present invention, the step of processing the physical characteristic change rate data and the chemical characteristic change rate data to obtain the physicochemical index change rate anomaly coefficient, and performing an activated carbon filtration module activation judgment response, includes:

[0103] Based on the color change rate data and odor intensity change rate data, combined with the chemical oxygen demand change rate data and biochemical oxygen demand change rate data, the abnormal coefficient of physicochemical index change rate is obtained.

[0104] The comparison results are obtained by comparing the abnormal coefficient of the change rate of the physical and chemical indicators with the preset abnormal threshold of the change rate of the physical and chemical indicators.

[0105] If the abnormal coefficient of the change rate of the physicochemical index is greater than the preset abnormal threshold of the change rate of the physicochemical index, the activated carbon filtration module of the target water treatment equipment is activated, and the filtration operation parameters of the activated carbon filtration module are adjusted accordingly.

[0106] The filtration operation parameters include activated carbon characteristic parameters and filtration system parameters.

[0107] It should be noted that the data on the rate of change of water color and the rate of change of odor intensity are combined with the data on the rate of change of chemical oxygen demand and the rate of change of biochemical oxygen demand to obtain the abnormal coefficient of the rate of change of the physicochemical indicators of water quality. If the abnormal amount of the rate of change of the physicochemical indicators of water quality exceeds the preset threshold, the filtration operation parameters of the activated carbon filtration module are adjusted and the activated carbon filtration module is started. The filtration operation parameters include the activated carbon characteristic parameters and the filtration system parameters.

[0108] The formula for calculating the abnormal coefficient of the rate of change of the physical and chemical index is as follows:

[0109] ;

[0110] in, The abnormal coefficient of the rate of change of physical and chemical indicators. , These are data on the rate of change in color and the rate of change in odor intensity, respectively. , These are data on the rate of change of chemical oxygen demand (COD) and the rate of change of biochemical oxygen demand (BOD), respectively. , , The preset correction coefficient can be determined through experimental fitting or theoretical derivation based on the actual needs of the scenario.

[0111] According to an embodiment of the present invention, it further includes:

[0112] The biometric change rate data is processed to obtain the biometric change rate anomaly coefficient, and a UV disinfection module activation judgment response is initiated, specifically including:

[0113] The abnormal coefficient of biological indicator change rate is obtained by processing the total bacterial count change rate data and the total coliform count change rate data.

[0114] The comparison results are obtained by comparing the abnormal coefficient of the rate of change of the biological indicator with the preset abnormal threshold of the rate of change of the biological indicator.

[0115] If the abnormal coefficient of the biological indicator change rate is greater than the preset abnormal threshold of the biological indicator change rate, then the ultraviolet disinfection module of the target water treatment equipment is activated, and the disinfection operation parameters of the ultraviolet disinfection module are adjusted accordingly.

[0116] The disinfection operation parameters include ultraviolet intensity parameters, water flow parameters, and lamp parameters;

[0117] The formula for calculating the abnormal coefficient of the rate of change of the biometrics is as follows:

[0118] ;

[0119] in, The abnormal coefficient of the rate of change of biological indicators. , These are data on the rate of change of total bacterial count and the rate of change of total coliform count, respectively. The preset correction coefficient can be determined through experimental fitting or theoretical derivation based on the actual needs of the scenario.

[0120] It should be noted that the data on the change rate of total bacteria count and total coliform count in the water are processed to obtain the abnormal coefficient of the change rate of biological indicators in the water. If the abnormal amount of the change rate of biological indicators in the water exceeds the preset threshold, the disinfection operation parameters of the live ultraviolet disinfection module are adjusted and the ultraviolet disinfection module is started. The disinfection operation parameters include ultraviolet intensity parameters, water flow parameters, and lamp parameters.

[0121] According to an embodiment of the present invention, it further includes:

[0122] Based on the influent parameters, product water parameters, and membrane performance parameters, the reverse osmosis capacity level of the equipment can be obtained by querying the preset water treatment equipment value level table.

[0123] Based on the activated carbon characteristic parameters and filtration system parameters, the equipment filtration capacity level is obtained by querying a preset water treatment equipment value level table.

[0124] Based on the ultraviolet intensity parameters, water flow parameters, and lamp parameters, the equipment disinfection capacity level is obtained by querying the preset water treatment equipment value level table.

[0125] The overall processing capacity coefficient of the equipment is obtained by weighting the equipment's reverse osmosis capacity level, filtration capacity level, and disinfection capacity level.

[0126] The water pollution coefficient at the outlet of the target water treatment equipment is obtained, and the water treatment effect coefficient is obtained by combining the real-time water pollution coefficient and the comprehensive treatment capacity coefficient of the equipment through a preset water quality effect evaluation model.

[0127] The water quality treatment effect coefficient is compared with the preset water quality treatment effect threshold, and the corresponding water quality treatment adjustment plan is obtained based on the comparison results.

[0128] It should be noted that after the decontamination and purification operation of the target water treatment equipment is completed, it is necessary to determine the effectiveness of the equipment. Therefore, firstly, the capacity level corresponding to each module during operation is retrieved from the equipment's capacity level table, including the reverse osmosis capacity level, filtration capacity level, and disinfection capacity level. Then, based on the capacity levels of the above three modules, the overall treatment capacity coefficient of the equipment is obtained. Furthermore, combined with the pollution degree coefficients corresponding to the inlet and outlet, a preset water quality effect evaluation model is used to obtain the water quality treatment effect coefficient of the equipment. The water quality effect evaluation model is a neural network model, which is trained on an initial water quality effect evaluation model based on a large number of historical effluent water pollution degree coefficients, inlet water pollution degree coefficients, and overall equipment treatment capacity coefficients to obtain a trained water quality effect evaluation model. If the coefficient is less than the preset value, it indicates that the equipment's water treatment effect is not good, and the water treatment plan needs to be readjusted, such as by maintaining or upgrading the water treatment equipment.

[0129] Secondly, the present invention also discloses a water quality monitoring and intelligent control system based on big data, including a memory and a processor. The memory includes a water quality monitoring and intelligent control method program based on big data. When the processor executes the water quality monitoring and intelligent control method program based on big data, it performs the following steps:

[0130] Monitor the water composition information at the inlet of the target water treatment equipment and extract real-time water quality characteristic data;

[0131] The real-time water quality characteristic data is processed to obtain the real-time water pollution coefficient, and it is determined whether the target water treatment equipment needs to be started.

[0132] If the target water treatment equipment needs to be started, the water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet are obtained, and the corresponding water quality characteristic change rate data are processed to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data.

[0133] The physical characteristic change rate data is processed to obtain the physical index change rate anomaly coefficient, and a reverse osmosis module start-up judgment response is performed.

[0134] The physical characteristic change rate data and chemical characteristic change rate data are processed to obtain the abnormal coefficient of the physicochemical index change rate, and the activated carbon filtration module start-up judgment response is performed.

[0135] The biological characteristic change rate data is processed to obtain the abnormal coefficient of the biological indicator change rate, and the ultraviolet disinfection module is activated and determined.

[0136] It should be noted that with the acceleration of industrialization and population growth, water pollution is becoming increasingly serious. Traditional water quality monitoring methods suffer from problems such as long monitoring cycles, inaccurate data, and inability to monitor in real time, failing to meet the needs of modern water resource management. Simultaneously, traditional water quality control methods also suffer from imprecise control and low efficiency. Therefore, a new water quality monitoring and intelligent control method is needed to improve the accuracy and real-time performance of water quality monitoring and achieve intelligent water quality control. In this embodiment, the water composition information at the inlet of the target water treatment equipment is first monitored, and real-time water quality characteristic data, including real-time physical characteristic data, real-time chemical characteristic data, and real-time biological characteristic data, is extracted. Based on the real-time water quality characteristic data, a real-time water pollution coefficient is obtained, and it is determined whether the target water treatment equipment needs to be activated. If activation is required, the target water treatment equipment is activated. The processing equipment acquires water quality characteristic data at the first and second preset time nodes at the inlet, and processes the data to obtain corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data, and biological characteristic change rate data. Based on the physical characteristic change rate data, it obtains the physical index change rate anomaly coefficient and initiates a start-up judgment response for the reverse osmosis module of the target water treatment equipment. Based on the physical and chemical characteristic change rate data, it obtains the physicochemical index change rate anomaly coefficient and initiates a start-up judgment response for the activated carbon filter module of the target water treatment equipment. Based on the biological characteristic change rate data, it obtains the biological index change rate anomaly coefficient and initiates a start-up judgment response for the ultraviolet disinfection module of the target water treatment equipment, thereby realizing a water quality monitoring and intelligent control technology based on big data.

[0137] According to an embodiment of the present invention, the monitoring of water composition information at the inlet of the target water treatment equipment and the extraction of real-time water quality characteristic data include:

[0138] Real-time monitoring of water composition information at the inlet of the target water treatment equipment, including physical composition information, chemical composition information and biological composition information;

[0139] Real-time water quality characteristic data, including real-time physical characteristic data, real-time chemical characteristic data, and real-time biological characteristic data, are extracted based on the physical component information, chemical component information, and biological component information.

[0140] The real-time physical characteristic data includes real-time salinity data, real-time hardness data, real-time heavy metal content data, real-time color data, and real-time odor intensity data.

[0141] The real-time chemical characteristic data includes real-time chemical oxygen demand data and real-time biochemical oxygen demand data;

[0142] The real-time biometric data includes real-time total bacterial count data and real-time total coliform count data.

[0143] It should be noted that before monitoring water quality, it is necessary to understand the composition of the water as much as possible. Therefore, it is necessary to monitor the water composition information at the inlet of the target water treatment equipment in real time, including physical, chemical and biological components. Based on the above information, real-time physical characteristic data, real-time chemical characteristic data and real-time biological characteristic data of the water quality should be extracted. Among them, the real-time physical characteristic data includes real-time salinity, real-time hardness, real-time heavy metal content, real-time color and real-time odor intensity data; the real-time chemical characteristic data includes real-time chemical oxygen demand and real-time biochemical oxygen demand data; and the real-time biological characteristic data includes real-time total bacteria count and real-time total coliform count data.

[0144] According to an embodiment of the present invention, the step of processing the real-time water quality characteristic data to obtain a real-time water pollution coefficient and determining whether the target water treatment equipment needs to be activated includes:

[0145] The real-time water quality characteristic data is processed by a preset water pollution degree detection model to obtain the real-time water pollution degree coefficient.

[0146] The comparison results are obtained by comparing the real-time water pollution coefficient with the preset water pollution threshold.

[0147] If the real-time water pollution coefficient is greater than the preset water pollution threshold, the target water treatment equipment will be activated.

[0148] It should be noted that the degree of water pollution needs to be assessed. When the water quality reaches a certain level of pollution, the target water treatment equipment needs to be activated to purify the water. Therefore, real-time water quality characteristic data is processed through a preset water pollution detection model to obtain a real-time water pollution coefficient. The water pollution detection model is a neural network model, which is trained on an initial water pollution detection model based on a large amount of historical water quality characteristic data to obtain a trained water pollution detection model. The model is then used to determine whether the coefficient exceeds a preset value. If it does, the target water treatment equipment needs to be activated.

[0149] According to an embodiment of the present invention, if it is necessary to start the target water treatment equipment, the water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet are acquired, and the corresponding water quality characteristic change rate data are processed to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data, including:

[0150] If it is necessary to start the target water treatment equipment, obtain the water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet;

[0151] Based on the water quality characteristic data corresponding to the first preset time node and the water quality characteristic data corresponding to the second preset time node, corresponding processing is performed to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data.

[0152] The physical characteristic change rate data includes salt content change rate data, hardness change rate data, heavy metal content change rate data, color change rate data, and odor intensity change rate data.

[0153] The chemical characteristic change rate data includes chemical oxygen demand change rate data and biochemical oxygen demand change rate data;

[0154] The biometric change rate data includes the total bacterial count change rate data and the total coliform count change rate data.

[0155] It should be noted that if it is determined that the target water treatment equipment needs to be activated for water purification, it is necessary to further understand the changes in water pollution indicators. If the corresponding water quality indicators gradually worsen over time, it indicates normal pollution, and the water purification work can be completed by operating the equipment according to the original conventional operating parameters. However, if the changes in water pollution indicators are rapid and sudden, it indicates that the water quality is affected by other pollution sources and the pollution is unconventional. Therefore, it is necessary to adjust the parameters of the corresponding treatment modules of the target water treatment equipment. The treatment modules of the target water treatment equipment include a reverse osmosis module, an activated carbon filtration module, and an ultraviolet disinfection module. Among them, the water quality characteristic change rate data includes physical characteristic change rate, chemical characteristic change rate, and biological characteristic change rate data. The physical characteristic change rate data includes the change rate of salinity, hardness, heavy metal content, color, and odor intensity. The chemical characteristic change rate data includes the change rate of chemical oxygen demand and biochemical oxygen demand. The biological characteristic change rate data includes the change rate of total bacteria count and total coliform count.

[0156] According to an embodiment of the present invention, the step of processing the physical characteristic change rate data to obtain the physical index change rate anomaly coefficient and performing a reverse osmosis module start-up determination response includes:

[0157] The physical index change rate anomaly coefficient is obtained by processing the data on salt content change rate, hardness change rate, and heavy metal content change rate.

[0158] The comparison results are obtained by comparing the abnormal coefficient of the rate of change of the physical index with the preset abnormal threshold of the rate of change of the physical index.

[0159] If the abnormal coefficient of the change rate of the physical index is greater than the preset abnormal threshold of the change rate of the physical index, then the reverse osmosis module of the target water treatment equipment is started, and the reverse osmosis operation parameters of the reverse osmosis module are adjusted accordingly.

[0160] The reverse osmosis operating parameters include feed water parameters, product water parameters, and membrane performance parameters.

[0161] It should be noted that the data on the rate of change of salinity, hardness and heavy metal content of water are processed to obtain the abnormal coefficient of the rate of change of physical indicators of water quality. If the abnormal amount of the rate of change of physical indicators of water quality exceeds the preset threshold, the reverse osmosis module's reverse osmosis operating parameters are adjusted and the reverse osmosis module is started. The reverse osmosis operating parameters include feed water parameters, product water parameters and membrane performance parameters.

[0162] The formula for calculating the anomaly coefficient of the rate of change of the physical index is as follows:

[0163] ;

[0164] in, This refers to the anomaly coefficient of the rate of change of physical indicators. , , These are data on the rate of change in salt content, the rate of change in hardness, and the rate of change in heavy metal content, respectively. The preset correction coefficient can be determined through experimental fitting or theoretical derivation based on the actual needs of the scenario.

[0165] According to an embodiment of the present invention, the step of processing the physical characteristic change rate data and the chemical characteristic change rate data to obtain the physicochemical index change rate anomaly coefficient, and performing an activated carbon filtration module activation judgment response, includes:

[0166] Based on the color change rate data and odor intensity change rate data, combined with the chemical oxygen demand change rate data and biochemical oxygen demand change rate data, the abnormal coefficient of physicochemical index change rate is obtained.

[0167] The comparison results are obtained by comparing the abnormal coefficient of the change rate of the physical and chemical indicators with the preset abnormal threshold of the change rate of the physical and chemical indicators.

[0168] If the abnormal coefficient of the change rate of the physicochemical index is greater than the preset abnormal threshold of the change rate of the physicochemical index, the activated carbon filtration module of the target water treatment equipment is activated, and the filtration operation parameters of the activated carbon filtration module are adjusted accordingly.

[0169] The filtration operation parameters include activated carbon characteristic parameters and filtration system parameters.

[0170] It should be noted that the data on the rate of change of water color and the rate of change of odor intensity are combined with the data on the rate of change of chemical oxygen demand and the rate of change of biochemical oxygen demand to obtain the abnormal coefficient of the rate of change of the physicochemical indicators of water quality. If the abnormal amount of the rate of change of the physicochemical indicators of water quality exceeds the preset threshold, the filtration operation parameters of the activated carbon filtration module are adjusted and the activated carbon filtration module is started. The filtration operation parameters include the activated carbon characteristic parameters and the filtration system parameters.

[0171] The formula for calculating the abnormal coefficient of the rate of change of the physical and chemical index is as follows:

[0172] ;

[0173] in, The abnormal coefficient of the rate of change of physical and chemical indicators. , These are data on the rate of change in color and the rate of change in odor intensity, respectively. , These are data on the rate of change of chemical oxygen demand (COD) and the rate of change of biochemical oxygen demand (BOD), respectively. , , The preset correction coefficient can be determined through experimental fitting or theoretical derivation based on the actual needs of the scenario.

[0174] According to an embodiment of the present invention, it further includes:

[0175] The biometric change rate data is processed to obtain the biometric change rate anomaly coefficient, and a UV disinfection module activation judgment response is initiated, specifically including:

[0176] The abnormal coefficient of biological indicator change rate is obtained by processing the total bacterial count change rate data and the total coliform count change rate data.

[0177] The comparison results are obtained by comparing the abnormal coefficient of the rate of change of the biological indicator with the preset abnormal threshold of the rate of change of the biological indicator.

[0178] If the abnormal coefficient of the biological indicator change rate is greater than the preset abnormal threshold of the biological indicator change rate, then the ultraviolet disinfection module of the target water treatment equipment is activated, and the disinfection operation parameters of the ultraviolet disinfection module are adjusted accordingly.

[0179] The disinfection operation parameters include ultraviolet intensity parameters, water flow parameters, and lamp parameters;

[0180] The formula for calculating the abnormal coefficient of the rate of change of the biometrics is as follows:

[0181] ;

[0182] in, The abnormal coefficient of the rate of change of biological indicators. , These are data on the rate of change of total bacterial count and the rate of change of total coliform count, respectively. The preset correction coefficient can be determined through experimental fitting or theoretical derivation based on the actual needs of the scenario.

[0183] It should be noted that the data on the change rate of total bacteria count and total coliform count in the water are processed to obtain the abnormal coefficient of the change rate of biological indicators in the water. If the abnormal amount of the change rate of biological indicators in the water exceeds the preset threshold, the disinfection operation parameters of the live ultraviolet disinfection module are adjusted and the ultraviolet disinfection module is started. The disinfection operation parameters include ultraviolet intensity parameters, water flow parameters, and lamp parameters.

[0184] According to an embodiment of the present invention, it further includes:

[0185] Based on the influent parameters, product water parameters, and membrane performance parameters, the reverse osmosis capacity level of the equipment can be obtained by querying the preset water treatment equipment value level table.

[0186] Based on the activated carbon characteristic parameters and filtration system parameters, the equipment filtration capacity level is obtained by querying a preset water treatment equipment value level table.

[0187] Based on the ultraviolet intensity parameters, water flow parameters, and lamp parameters, the equipment disinfection capacity level is obtained by querying the preset water treatment equipment value level table.

[0188] The overall processing capacity coefficient of the equipment is obtained by weighting the equipment's reverse osmosis capacity level, filtration capacity level, and disinfection capacity level.

[0189] The water pollution coefficient at the outlet of the target water treatment equipment is obtained, and the water treatment effect coefficient is obtained by combining the real-time water pollution coefficient and the comprehensive treatment capacity coefficient of the equipment through a preset water quality effect evaluation model.

[0190] The water quality treatment effect coefficient is compared with the preset water quality treatment effect threshold, and the corresponding water quality treatment adjustment plan is obtained based on the comparison results.

[0191] It should be noted that after the decontamination and purification operation of the target water treatment equipment is completed, it is necessary to determine the effectiveness of the equipment. Therefore, firstly, the capacity level corresponding to each module during operation is retrieved from the equipment's capacity level table, including the reverse osmosis capacity level, filtration capacity level, and disinfection capacity level. Then, based on the capacity levels of the above three modules, the overall treatment capacity coefficient of the equipment is obtained. Furthermore, combined with the pollution degree coefficients corresponding to the inlet and outlet, a preset water quality effect evaluation model is used to obtain the water quality treatment effect coefficient of the equipment. The water quality effect evaluation model is a neural network model, which is trained on an initial water quality effect evaluation model based on a large number of historical effluent water pollution degree coefficients, inlet water pollution degree coefficients, and overall equipment treatment capacity coefficients to obtain a trained water quality effect evaluation model. If the coefficient is less than the preset value, it indicates that the equipment's water treatment effect is not good, and the water treatment plan needs to be readjusted, such as by maintaining or upgrading the water treatment equipment.

[0192] A third aspect of the present invention provides a readable storage medium storing a water quality monitoring and intelligent control method program based on big data. When the big data-based water quality monitoring and intelligent control method program is executed by a processor, it implements the steps of the big data-based water quality monitoring and intelligent control method as described in any of the preceding claims.

[0193] This invention discloses a water quality monitoring and intelligent control method, system, and medium based on big data. It monitors water composition information at the inlet of a target water treatment device and extracts real-time water quality characteristic data, including real-time physical characteristic data, real-time chemical characteristic data, and real-time biological characteristic data. The real-time water quality characteristic data is processed to obtain a real-time water pollution coefficient, and a determination is made as to whether the target water treatment device needs to be activated. If activation is required, water quality characteristic data at a first preset time node and a second preset time node at the inlet are acquired and processed to obtain corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data, and biological characteristic change rate data. The system processes physical characteristic change rate data and biological characteristic change rate data, obtains anomaly coefficients for physical index change rate, and initiates a response to activate the reverse osmosis module of the target water treatment equipment. It also processes physical and chemical characteristic change rate data to obtain anomaly coefficients for physicochemical index change rate and initiates a response to activate the activated carbon filter module of the target water treatment equipment. Finally, it processes biological characteristic change rate data to obtain anomaly coefficients for biological index change rate and initiates a response to activate the ultraviolet disinfection module of the target water treatment equipment. This enables water quality monitoring and intelligent control technology based on big data.

[0194] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0195] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0196] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0197] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0198] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A water quality monitoring and intelligent control method based on big data, characterized in that, Includes the following steps: Monitor the water composition information at the inlet of the target water treatment equipment and extract real-time water quality characteristic data; The real-time water quality characteristic data is processed to obtain the real-time water pollution coefficient, and it is determined whether the target water treatment equipment needs to be started. If it is necessary to start the target water treatment equipment, obtain the water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet; Based on the water quality characteristic data corresponding to the first preset time node and the water quality characteristic data corresponding to the second preset time node, corresponding processing is performed to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data. The physical characteristic change rate data includes salt content change rate data, hardness change rate data, heavy metal content change rate data, color change rate data, and odor intensity change rate data. The chemical characteristic change rate data includes chemical oxygen demand change rate data and biochemical oxygen demand change rate data; The biomarker change rate data includes the total bacterial count change rate data and the total coliform count change rate data; The physical index change rate anomaly coefficient is obtained by processing the data on salt content change rate, hardness change rate, and heavy metal content change rate. The comparison results are obtained by comparing the abnormal coefficient of the rate of change of the physical index with the preset abnormal threshold of the rate of change of the physical index. If the abnormal coefficient of the change rate of the physical index is greater than the preset abnormal threshold of the change rate of the physical index, then the reverse osmosis module of the target water treatment equipment is started, and the reverse osmosis operation parameters of the reverse osmosis module are adjusted accordingly. The reverse osmosis operating parameters include feed water parameters, product water parameters, and membrane performance parameters; Based on the color change rate data and odor intensity change rate data, combined with the chemical oxygen demand change rate data and biochemical oxygen demand change rate data, the abnormal coefficient of physicochemical index change rate is obtained. The comparison results are obtained by comparing the abnormal coefficient of the change rate of the physical and chemical indicators with the preset abnormal threshold of the change rate of the physical and chemical indicators. If the abnormal coefficient of the change rate of the physicochemical index is greater than the preset abnormal threshold of the change rate of the physicochemical index, the activated carbon filtration module of the target water treatment equipment is activated, and the filtration operation parameters of the activated carbon filtration module are adjusted accordingly. The filtration operation parameters include activated carbon characteristic parameters and filtration system parameters; The abnormal coefficient of biological indicator change rate is obtained by processing the total bacterial count change rate data and the total coliform count change rate data. The comparison results are obtained by comparing the abnormal coefficient of the rate of change of the biological indicator with the preset abnormal threshold of the rate of change of the biological indicator. If the abnormal coefficient of the biological indicator change rate is greater than the preset abnormal threshold of the biological indicator change rate, then the ultraviolet disinfection module of the target water treatment equipment is activated, and the disinfection operation parameters of the ultraviolet disinfection module are adjusted accordingly. The disinfection operation parameters include ultraviolet intensity parameters, water flow parameters, and lamp parameters; Based on the influent parameters, product water parameters, and membrane performance parameters, the reverse osmosis capacity level of the equipment can be obtained by querying the preset water treatment equipment value level table. Based on the activated carbon characteristic parameters and filtration system parameters, the equipment filtration capacity level is obtained by querying a preset water treatment equipment value level table. Based on the ultraviolet intensity parameters, water flow parameters, and lamp parameters, the equipment disinfection capacity level is obtained by querying the preset water treatment equipment value level table. The overall processing capacity coefficient of the equipment is obtained by weighting the equipment's reverse osmosis capacity level, filtration capacity level, and disinfection capacity level. The water pollution coefficient at the outlet of the target water treatment equipment is obtained, and the water treatment effect coefficient is obtained by combining the real-time water pollution coefficient and the comprehensive treatment capacity coefficient of the equipment through a preset water quality effect evaluation model. The water quality treatment effect coefficient is compared with the preset water quality treatment effect threshold, and the corresponding water quality treatment adjustment plan is obtained based on the comparison results.

2. The water quality monitoring and intelligent control method based on big data according to claim 1, characterized in that, The monitoring unit identifies the water composition at the inlet of the target water treatment equipment and extracts real-time water quality characteristic data, including: Real-time monitoring of water composition information at the inlet of the target water treatment equipment, including physical composition information, chemical composition information and biological composition information; Real-time water quality characteristic data, including real-time physical characteristic data, real-time chemical characteristic data, and real-time biological characteristic data, are extracted based on the physical component information, chemical component information, and biological component information. The real-time physical characteristic data includes real-time salinity data, real-time hardness data, real-time heavy metal content data, real-time color data, and real-time odor intensity data. The real-time chemical characteristic data includes real-time chemical oxygen demand data and real-time biochemical oxygen demand data; The real-time biometric data includes real-time total bacterial count data and real-time total coliform count data.

3. The water quality monitoring and intelligent control method based on big data according to claim 2, characterized in that, The step of processing the real-time water quality characteristic data to obtain the real-time water pollution coefficient and determining whether the target water treatment equipment needs to be activated includes: The real-time water quality characteristic data is processed by a preset water pollution degree detection model to obtain the real-time water pollution degree coefficient. The comparison results are obtained by comparing the real-time water pollution coefficient with the preset water pollution threshold. If the real-time water pollution coefficient is greater than the preset water pollution threshold, the target water treatment equipment will be activated.

4. A water quality monitoring and intelligent control system based on big data, characterized in that, The system includes a memory and a processor. The memory contains a program for a water quality monitoring and intelligent control method based on big data. When the processor executes the program for the water quality monitoring and intelligent control method based on big data, it performs the following steps: Monitor the water composition information at the inlet of the target water treatment equipment and extract real-time water quality characteristic data; The real-time water quality characteristic data is processed to obtain the real-time water pollution coefficient, and it is determined whether the target water treatment equipment needs to be started. If it is necessary to start the target water treatment equipment, obtain the water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet; Based on the water quality characteristic data corresponding to the first preset time node and the water quality characteristic data corresponding to the second preset time node, corresponding processing is performed to obtain the corresponding water quality characteristic change rate data, including physical characteristic change rate data, chemical characteristic change rate data and biological characteristic change rate data. The physical characteristic change rate data includes salt content change rate data, hardness change rate data, heavy metal content change rate data, color change rate data, and odor intensity change rate data. The chemical characteristic change rate data includes chemical oxygen demand change rate data and biochemical oxygen demand change rate data; The biomarker change rate data includes the total bacterial count change rate data and the total coliform count change rate data; The physical index change rate anomaly coefficient is obtained by processing the data on salt content change rate, hardness change rate, and heavy metal content change rate. The comparison results are obtained by comparing the abnormal coefficient of the rate of change of the physical index with the preset abnormal threshold of the rate of change of the physical index. If the abnormal coefficient of the change rate of the physical index is greater than the preset abnormal threshold of the change rate of the physical index, then the reverse osmosis module of the target water treatment equipment is started, and the reverse osmosis operation parameters of the reverse osmosis module are adjusted accordingly. The reverse osmosis operating parameters include feed water parameters, product water parameters, and membrane performance parameters; Based on the color change rate data and odor intensity change rate data, combined with the chemical oxygen demand change rate data and biochemical oxygen demand change rate data, the abnormal coefficient of physicochemical index change rate is obtained. The comparison results are obtained by comparing the abnormal coefficient of the change rate of the physical and chemical indicators with the preset abnormal threshold of the change rate of the physical and chemical indicators. If the abnormal coefficient of the change rate of the physicochemical index is greater than the preset abnormal threshold of the change rate of the physicochemical index, the activated carbon filtration module of the target water treatment equipment is activated, and the filtration operation parameters of the activated carbon filtration module are adjusted accordingly. The filtration operation parameters include activated carbon characteristic parameters and filtration system parameters; The abnormal coefficient of biological indicator change rate is obtained by processing the total bacterial count change rate data and the total coliform count change rate data. The comparison results are obtained by comparing the abnormal coefficient of the rate of change of the biological indicator with the preset abnormal threshold of the rate of change of the biological indicator. If the abnormal coefficient of the biological indicator change rate is greater than the preset abnormal threshold of the biological indicator change rate, then the ultraviolet disinfection module of the target water treatment equipment is activated, and the disinfection operation parameters of the ultraviolet disinfection module are adjusted accordingly. The disinfection operation parameters include ultraviolet intensity parameters, water flow parameters, and lamp parameters; Based on the influent parameters, product water parameters, and membrane performance parameters, the reverse osmosis capacity level of the equipment can be obtained by querying the preset water treatment equipment value level table. Based on the activated carbon characteristic parameters and filtration system parameters, the equipment filtration capacity level is obtained by querying a preset water treatment equipment value level table. Based on the ultraviolet intensity parameters, water flow parameters, and lamp parameters, the equipment disinfection capacity level is obtained by querying the preset water treatment equipment value level table. The overall processing capacity coefficient of the equipment is obtained by weighting the equipment's reverse osmosis capacity level, filtration capacity level, and disinfection capacity level. The water pollution coefficient at the outlet of the target water treatment equipment is obtained, and the water treatment effect coefficient is obtained by combining the real-time water pollution coefficient and the comprehensive treatment capacity coefficient of the equipment through a preset water quality effect evaluation model. The water quality treatment effect coefficient is compared with the preset water quality treatment effect threshold, and the corresponding water quality treatment adjustment plan is obtained based on the comparison results.

5. The water quality monitoring and intelligent control system based on big data according to claim 4, characterized in that, The monitoring unit identifies the water composition at the inlet of the target water treatment equipment and extracts real-time water quality characteristic data, including: Real-time monitoring of water composition information at the inlet of the target water treatment equipment, including physical composition information, chemical composition information and biological composition information; Real-time water quality characteristic data, including real-time physical characteristic data, real-time chemical characteristic data, and real-time biological characteristic data, are extracted based on the physical component information, chemical component information, and biological component information. The real-time physical characteristic data includes real-time salinity data, real-time hardness data, real-time heavy metal content data, real-time color data, and real-time odor intensity data. The real-time chemical characteristic data includes real-time chemical oxygen demand data and real-time biochemical oxygen demand data; The real-time biometric data includes real-time total bacterial count data and real-time total coliform count data.

6. The water quality monitoring and intelligent control system based on big data according to claim 5, characterized in that, The step of processing the real-time water quality characteristic data to obtain the real-time water pollution coefficient and determining whether the target water treatment equipment needs to be activated includes: The real-time water quality characteristic data is processed by a preset water pollution degree detection model to obtain the real-time water pollution degree coefficient. The comparison results are obtained by comparing the real-time water pollution coefficient with the preset water pollution threshold. If the real-time water pollution coefficient is greater than the preset water pollution threshold, the target water treatment equipment will be activated.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a water quality monitoring and intelligent control method program based on big data. When the big data-based water quality monitoring and intelligent control method program is executed by a processor, it implements the steps of the big data-based water quality monitoring and intelligent control method as described in any one of claims 1 to 3.

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