Water quality monitoring and intelligent control method and system based on big data and medium

Abstract

The invention 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 target water treatment equipment, extracting real-time water quality characteristic data at a water inlet, processing to obtain a real-time water quality pollution degree coefficient, judging whether the target water treatment equipment needs to be started or not, if so, obtaining water quality characteristic data corresponding to a first preset time node and a second preset time node at the water inlet, processing to obtain water quality characteristic change rate data, and respectively performing corresponding processing according to the physical characteristic change rate data, the chemical characteristic change rate data and the biological characteristic change rate data to obtain a corresponding physical index change rate abnormal coefficient, a corresponding physicochemical index change rate abnormal coefficient and a corresponding biological index change rate abnormal coefficient; and a reverse osmosis module, an activated carbon filtration module and an ultraviolet disinfection module of the target water treatment equipment are respectively subjected to start judgment response, so that a water quality monitoring and intelligent control technology based on big data is realized.

Description

Technical Field

[0001] The present application relates to the field of water quality monitoring technology, and more specifically, to a method, system and medium for water quality monitoring and intelligent control based on big data. Background Art

[0002] With the acceleration of industrialization and population growth, water pollution is becoming increasingly serious. Traditional water quality monitoring methods have problems such as long monitoring cycles, inaccurate data, and inability to monitor in real time, which cannot meet the needs of modern water resource management. At the same time, 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 realize intelligent control of water quality.

[0004] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0005] The purpose of the present application is to provide a water quality monitoring and intelligent control method, system and medium based on big data. It can judge whether the target water treatment equipment needs to be started according to the real-time water quality pollution coefficient at the water inlet of the target water treatment equipment. If it needs to be started, the reverse osmosis module, activated carbon filtration module and ultraviolet disinfection module of the target water treatment equipment are respectively started and judged to respond according to the obtained physical indicator change rate abnormal coefficient, physicochemical indicator change rate abnormal coefficient and biological indicator change rate abnormal coefficient, so as to realize 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: Monitor the water quality composition information at the water inlet of the target water treatment equipment and extract real-time water quality characteristic data; Processing the real-time water quality characteristic data to obtain a real-time water quality pollution coefficient, and determining 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, and process 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; Processing the physical characteristic change rate data to obtain the physical indicator change rate abnormality coefficient, and performing a reverse osmosis module startup determination response; Processing the physical characteristic change rate data and the chemical characteristic change rate data to obtain the abnormal coefficient of the physical and chemical index change rate, and performing a start-up judgment response of the activated carbon filtration module; The biological characteristic change rate data is processed to obtain the abnormal coefficient of the biological indicator change rate, and the ultraviolet disinfection module startup determination response is performed.

[0007] Optionally, in the water quality monitoring and intelligent control method based on big data described in the present application, the water quality component information at the water inlet of the monitoring target water treatment equipment and extracting real-time water quality characteristic data include: Real-time monitoring of water quality component information at the water inlet of the target water treatment equipment, including physical component information, chemical component information and biological component information; Extracting real-time water quality characteristic data according to 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; The real-time physical characteristic data includes real-time salt content data, real-time hardness data, real-time heavy metal content data, real-time chromaticity 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 bacteria count data and real-time total coliform count data.

[0008] Optionally, in the water quality monitoring and intelligent control method based on big data described in the present application, the processing according to the real-time water quality characteristic data to obtain the real-time water quality pollution coefficient and determine whether the target water treatment equipment needs to be started include: 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; Comparing the real-time water pollution coefficient with a preset water pollution threshold to obtain a comparison result; If the real-time water quality pollution coefficient is greater than the preset water quality pollution threshold, the target water treatment equipment is started.

[0009] Optionally, in the water quality monitoring and intelligent control method based on big data described in the present application, 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 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: If it is necessary to start the target water treatment equipment, obtain water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet; According to 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 respectively 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 physical characteristic change rate data include salt content change rate data, hardness change rate data, heavy metal content change rate data, chromaticity 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 biological characteristic change rate data includes total bacteria count change rate data and total coliform count change rate data.

[0010] Optionally, in the water quality monitoring and intelligent control method based on big data described in the present application, the processing according to the physical characteristic change rate data to obtain the abnormal coefficient of the physical indicator change rate and perform a reverse osmosis module startup determination response include: Processing the salt content change rate data, the hardness change rate data and the heavy metal content change rate data to obtain the abnormal coefficient of the physical index change rate; Comparing the physical indicator change rate abnormality coefficient with a preset physical indicator change rate abnormality threshold to obtain a comparison result; If the physical indicator change rate abnormal coefficient is greater than the preset physical indicator change rate abnormal threshold, 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 operation parameters include water inlet parameters, water production parameters and membrane performance parameters.

[0011] Optionally, in the water quality monitoring and intelligent control method based on big data described in the present application, the physical characteristic change rate data and the chemical characteristic change rate data are processed to obtain the abnormal coefficient of the physicochemical index change rate, and the activated carbon filtration module is started to determine the response, including: According to the chromaticity change rate data and the odor intensity change rate data, combined with the chemical oxygen demand change rate data and the biochemical oxygen demand change rate data, the abnormal coefficient of the physical and chemical index change rate is obtained; Comparing the abnormal coefficient of the physicochemical index change rate with a preset abnormal threshold of the physicochemical index change rate to obtain a comparison result; If the abnormal coefficient of the physicochemical index change rate is greater than the preset physicochemical index change rate abnormal threshold, the activated carbon filter module of the target water treatment equipment is started, and the filtering operation parameters of the activated carbon filter module are adjusted accordingly; The filtering operation parameters include activated carbon characteristic parameters and filtering system parameters.

[0012] In a second aspect, the present application provides a water quality monitoring and intelligent control system based on big data, the system comprising: a memory and a processor, the memory comprising a program of a water quality monitoring and intelligent control method based on big data, the program of the water quality monitoring and intelligent control method based on big data being executed by the processor to implement the following steps: Monitor the water quality composition information at the water inlet of the target water treatment equipment and extract real-time water quality characteristic data; Processing the real-time water quality characteristic data to obtain a real-time water quality pollution coefficient, and determining 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, and process 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; Processing the physical characteristic change rate data to obtain the physical indicator change rate abnormality coefficient, and performing a reverse osmosis module startup determination response; Processing the physical characteristic change rate data and the chemical characteristic change rate data to obtain the abnormal coefficient of the physical and chemical index change rate, and performing a start-up judgment response of the activated carbon filtration module; The biological characteristic change rate data is processed to obtain the abnormal coefficient of the biological indicator change rate, and the ultraviolet disinfection module startup determination response is performed.

[0013] Optionally, in the water quality monitoring and intelligent control system based on big data described in the present application, the water quality component information at the water inlet of the monitoring target water treatment equipment and the extraction of real-time water quality characteristic data include: Real-time monitoring of water quality component information at the water inlet of the target water treatment equipment, including physical component information, chemical component information and biological component information; Extracting real-time water quality characteristic data according to 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; The real-time physical characteristic data includes real-time salt content data, real-time hardness data, real-time heavy metal content data, real-time chromaticity 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 bacteria count data and real-time total coliform count data.

[0014] Optionally, in the water quality monitoring and intelligent control system based on big data described in the present application, the processing according to the real-time water quality characteristic data to obtain the real-time water quality pollution coefficient and determine whether the target water treatment equipment needs to be started include: 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; Comparing the real-time water pollution coefficient with a preset water pollution threshold to obtain a comparison result; If the real-time water quality pollution coefficient is greater than the preset water quality pollution threshold, the target water treatment equipment is started.

[0015] In the third aspect, the present application also provides a computer-readable storage medium, which stores a water quality monitoring and intelligent control method program based on big data. When the water quality monitoring and intelligent control method program based on big data is executed by a processor, the steps of the water quality monitoring and intelligent control method based on big data as described in any one of the above items are implemented.

[0016] As can be seen from the above, the water quality monitoring and intelligent control method, system and medium based on big data provided by the present application monitor the water quality component information at the water 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, process the real-time water quality characteristic data, obtain the real-time water quality pollution coefficient, and judge whether it is necessary to start the target water treatment equipment. 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 to obtain the corresponding water quality characteristic change rate data, including the physical characteristic change rate data. According to the data, chemical characteristic change rate data and biological characteristic change rate data, the physical characteristic change rate data is processed to obtain the abnormal coefficient of the physical indicator change rate, and the reverse osmosis module of the target water treatment equipment is started and judged in response. According to the physical characteristic change rate data and the chemical characteristic change rate data, the physicochemical indicator change rate abnormal coefficient is processed to obtain the abnormal coefficient of the biological indicator change rate, and the ultraviolet disinfection module of the target water treatment equipment is started and judged in response, thereby realizing the water quality monitoring and intelligent control technology based on big data.

[0017] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flow chart of a water quality monitoring and intelligent control method based on big data provided in an embodiment of the present application; Figure 2 A flow chart of extracting real-time water quality characteristic data for a water quality monitoring and intelligent control method based on big data provided in an embodiment of the present application; Figure 3 A flow chart of the water quality monitoring and intelligent control method based on big data provided in an embodiment of the present application for determining whether the target water treatment equipment needs to be started; Figure 4 A flow chart of obtaining corresponding water quality characteristic change rate data for the water quality monitoring and intelligent control method based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0021] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0022] Please refer to Figure 1 , Figure 1 1 is a flow chart of a water quality monitoring and intelligent control method based on big data in some embodiments of the present application. The water quality monitoring and intelligent control method based on big data is used in terminal devices, such as computers, mobile phone terminals, etc. The water quality monitoring and intelligent control method based on big data includes the following steps: S11, monitoring water quality component information at the water inlet of the target water treatment equipment, and extracting real-time water quality characteristic data; S12, processing the real-time water quality characteristic data to obtain a real-time water quality pollution coefficient, and determining whether it is necessary to start the target water treatment equipment; 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 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; S14, processing the physical characteristic change rate data to obtain the abnormal coefficient of the physical indicator change rate, and performing a reverse osmosis module startup determination response; S15, processing the physical characteristic change rate data and the chemical characteristic change rate data to obtain the abnormal coefficient of the physicochemical index change rate, and performing a start-up determination response of the activated carbon filtration module; S16. Process the biological characteristic change rate data to obtain the abnormal coefficient of the biological indicator change rate, and perform a start-up determination response for the ultraviolet disinfection module.

[0023] It should be noted that with the acceleration of industrialization and the growth of population, the pollution problem of water resources is becoming increasingly serious. Traditional water quality monitoring methods have problems such as long monitoring cycle, inaccurate data, and inability to monitor in real time, which cannot meet the needs of modern water resource management. At the same time, traditional water quality control methods also have problems such as inaccurate 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 realize intelligent control of water quality. In this embodiment, the water quality component information at the water inlet of the target water treatment equipment is first monitored, and real-time water quality characteristic data is extracted, 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 the real-time water quality pollution coefficient, and it is determined whether the target water treatment equipment needs to be started. If the target water needs to be started, A processing device obtains water quality characteristic data corresponding to a first preset time node and a second preset time node at a water inlet, and processes 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, processes according to the physical characteristic change rate data, obtains the abnormal coefficient of the physical indicator change rate, and makes a start-up judgment response to the reverse osmosis module of the target water treatment equipment, processes according to the physical characteristic change rate data and the chemical characteristic change rate data, obtains the abnormal coefficient of the physicochemical indicator change rate, and makes a start-up judgment response to the activated carbon filtration module of the target water treatment equipment, processes according to the biological characteristic change rate data, obtains the abnormal coefficient of the biological indicator change rate, and makes a start-up judgment response to the ultraviolet disinfection module of the target water treatment equipment, thereby realizing the technology of water quality monitoring and intelligent control based on big data.

[0024] Please refer to Figure 2 , Figure 2 The flowchart of extracting real-time water quality characteristic data of the water quality monitoring and intelligent control method based on big data in some embodiments of the present application. According to an embodiment of the present invention, the water quality component information at the water inlet of the target water treatment equipment is monitored and the real-time water quality characteristic data is extracted, including: S21, real-time monitoring of water quality component information at the water inlet of the target water treatment equipment, including physical component information, chemical component information and biological component information; S22, extracting real-time water quality characteristic data according to 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; S23, the real-time physical characteristic data includes real-time salt content data, real-time hardness data, real-time heavy metal content data, real-time color data and real-time odor intensity data; S24, the real-time chemical characteristic data includes real-time chemical oxygen demand data and real-time biochemical oxygen demand data; S25. The real-time biometric data includes real-time total bacteria count data and real-time total coliform count data.

[0025] It should be noted that before monitoring the water quality, it is necessary to understand the composition of the water as much as possible. Therefore, it is necessary to monitor the water quality component information at the water inlet of the target water treatment equipment in real time, including physical composition, chemical composition and biological composition information, and extract the real-time physical characteristic data, real-time chemical characteristic data and real-time biological characteristic data of the water quality according to the above information. Among them, the real-time physical characteristic data include real-time salt content, real-time hardness, real-time heavy metal content, real-time color and real-time odor intensity data, the real-time chemical characteristic data include real-time chemical oxygen demand and real-time biochemical oxygen demand data, and the real-time biological characteristic data include real-time total bacteria count and real-time total coliform count data.

[0026] Please refer to Figure 3 , Figure 3 The present invention is a flowchart of a method for water quality monitoring and intelligent control based on big data in some embodiments of the present application for determining whether the target water treatment equipment needs to be started. According to an embodiment of the present invention, the real-time water quality characteristic data is processed to obtain a real-time water quality pollution coefficient, and determine whether the target water treatment equipment needs to be started, including: S31, processing the real-time water quality characteristic data through a preset water quality pollution degree detection model to obtain a real-time water quality pollution degree coefficient; S32, comparing the real-time water pollution coefficient with a preset water pollution threshold to obtain a comparison result; S33: If the real-time water pollution coefficient is greater than a preset water pollution threshold, the target water treatment equipment is started.

[0027] It should be noted that it is necessary to judge the degree of water pollution. When the water quality is polluted to a certain extent, it is necessary to start the target water treatment equipment to decontaminate and purify the water. Therefore, the real-time water quality characteristic data is processed through a preset water quality pollution detection model to obtain a real-time water quality pollution coefficient. The water quality pollution detection model is a neural network model. The initialized water quality pollution detection model is trained based on a large amount of historical water quality characteristic data to obtain a trained water quality pollution detection model and determine whether the coefficient exceeds the preset value. If it exceeds the preset value, the target water treatment equipment needs to be started.

[0028] Please refer to Figure 4 , Figure 4It is a flow chart of obtaining corresponding water quality characteristic change rate data of the water quality monitoring and intelligent control method based on big data in some embodiments of the present application. According to an embodiment of the present invention, 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 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: S41, if the target water treatment equipment needs to be started, obtaining water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet; S42, performing corresponding processing 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, 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; S43, the physical characteristic change rate data includes salt content change rate data, hardness change rate data, heavy metal content change rate data, chromaticity change rate data and odor intensity change rate data; S44, the chemical characteristic change rate data includes chemical oxygen demand change rate data and biochemical oxygen demand change rate data; S45. The biological characteristic change rate data includes total bacteria count change rate data and total coliform count change rate data.

[0029] It should be noted that if it is determined that the target water treatment equipment needs to be started to purify the water, the changes in the water pollution indicators should be further understood. If the corresponding water quality indicators gradually become more serious over time, it means that they are normally polluted, and the water purification work can be completed by operating according to the original conventional equipment operating parameters. However, if the changes in the water pollution indicators are rapid and sudden, it means that the water quality is polluted by other sources and the pollution is unconventional. Therefore, it is necessary to adjust the parameters of the processing module corresponding to the target water treatment equipment accordingly. The processing modules of the target water treatment equipment include reverse osmosis modules, activated carbon filtration modules and ultraviolet disinfection modules. Among them, the water quality characteristic change rate data include physical characteristic change rate, chemical characteristic change rate and biological characteristic change rate data. The physical characteristic change rate data include salt content change rate, hardness change rate, heavy metal content change rate, chromaticity change rate and odor intensity change rate data. The chemical characteristic change rate data include chemical oxygen demand change rate and biochemical oxygen demand change rate data. The biological characteristic change rate data includes total bacteria change rate and total coliform count change rate data.

[0030] According to an embodiment of the present invention, the processing according to the physical characteristic change rate data to obtain the physical indicator change rate abnormality coefficient and perform a reverse osmosis module startup determination response includes: Processing the salt content change rate data, the hardness change rate data and the heavy metal content change rate data to obtain the abnormal coefficient of the physical index change rate; Comparing the physical indicator change rate abnormality coefficient with a preset physical indicator change rate abnormality threshold to obtain a comparison result; If the physical indicator change rate abnormal coefficient is greater than the preset physical indicator change rate abnormal threshold, 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 operation parameters include water inlet parameters, water production parameters and membrane performance parameters.

[0031] It should be noted that, according to the salt content change rate data, hardness change rate data and heavy metal content change rate data of water quality, the abnormal coefficient of the change rate of the physical index of water quality is obtained. If the abnormal amount of the change rate of the physical index of water quality exceeds the preset threshold, the reverse osmosis operation parameters of the reverse osmosis module are adjusted and the reverse osmosis module is started, wherein the reverse osmosis operation parameters include water inlet parameters, water production parameters and membrane performance parameters; The calculation formula of the abnormal coefficient of the physical index change rate is: ; in, is the abnormal coefficient of the change rate of physical indicators, , , They are the salt content change rate data, hardness change rate data, and heavy metal content change rate data. It is a preset correction coefficient, and its value can be determined through experimental fitting or theoretical derivation according to actual scene requirements.

[0032] According to an embodiment of the present invention, the processing according to the physical characteristic change rate data and the chemical characteristic change rate data to obtain the abnormal coefficient of the physicochemical index change rate and to perform an activated carbon filter module startup determination response includes: According to the chromaticity change rate data and the odor intensity change rate data, combined with the chemical oxygen demand change rate data and the biochemical oxygen demand change rate data, the abnormal coefficient of the physical and chemical index change rate is obtained; Comparing the abnormal coefficient of the physicochemical index change rate with a preset abnormal threshold of the physicochemical index change rate to obtain a comparison result; If the abnormal coefficient of the physicochemical index change rate is greater than the preset physicochemical index change rate abnormal threshold, the activated carbon filter module of the target water treatment equipment is started, and the filtering operation parameters of the activated carbon filter module are adjusted accordingly; The filtering operation parameters include activated carbon characteristic parameters and filtering system parameters.

[0033] It should be noted that, according to the chromaticity change rate data and the odor intensity change rate data of the water quality, combined with the chemical oxygen demand change rate data and the biochemical oxygen demand change rate data, the abnormal coefficient of the change rate of the physical and chemical indicators of the water quality is obtained. If the abnormal amount of the change rate of the physical and chemical indicators of the water quality exceeds the preset threshold, the filtering operation parameters of the activated carbon filtering module are adjusted and the activated carbon filtering module is started, wherein the filtering operation parameters include the activated carbon characteristic parameters and the filtering system parameters; The calculation formula of the abnormal coefficient of the physical and chemical index change rate is: ; in, is the abnormal coefficient of the change rate of physical and chemical indicators, , They are the data of chromaticity change rate and odor intensity change rate, , They are the chemical oxygen demand change rate data and the biochemical oxygen demand change rate data. , , It is a preset correction coefficient, and its value can be determined through experimental fitting or theoretical derivation according to actual scene requirements.

[0034] According to an embodiment of the present invention, it also includes: Processing is performed according to the biological characteristic change rate data to obtain the abnormal coefficient of the biological indicator change rate, and a UV disinfection module start-up determination response is performed, specifically including: Processing the total bacteria count change rate data and the total coliform count change rate data to obtain a biological indicator change rate abnormality coefficient; Comparing the biological indicator change rate abnormality coefficient with a preset biological indicator change rate abnormality threshold to obtain a comparison result; If the biological indicator change rate abnormal coefficient is greater than the preset biological indicator change rate abnormal threshold, the ultraviolet disinfection module of the target water treatment equipment is started, 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; The calculation formula of the abnormal coefficient of the biological indicator change rate is: ; in, is the abnormal coefficient of biological index change rate, , They are the total bacterial count change rate data and the total coliform count change rate data. It is a preset correction coefficient, and its value can be determined through experimental fitting or theoretical derivation according to actual scene requirements.

[0035] It should be noted that the water quality biological indicator change rate abnormality coefficient is obtained by processing the total bacteria count change rate data and the total coliform count change rate data. If the abnormal amount of the water quality biological indicator change rate exceeds the preset threshold, the disinfection operation parameters of the active ultraviolet disinfection module are adjusted and the ultraviolet disinfection module is started, wherein the disinfection operation parameters include ultraviolet intensity parameters, water flow parameters and lamp parameters.

[0036] According to an embodiment of the present invention, it also includes: According to the water inlet parameters, water production parameters and membrane performance parameters, a preset water treatment equipment value level table is queried to obtain the equipment reverse osmosis capacity level; According to the activated carbon characteristic parameters and the filtration system parameters, a preset water treatment equipment value level table is queried to obtain the equipment filtration capacity level; According to the ultraviolet intensity parameter, water flow parameter and lamp tube parameter, a preset water treatment equipment value level table is queried to obtain the equipment disinfection capacity level; Perform weighted processing according to the equipment's reverse osmosis capacity level, equipment's filtration capacity level, and equipment's disinfection capacity level to obtain the equipment's comprehensive processing capacity coefficient; Obtaining the water quality pollution coefficient of the outlet water at the outlet of the target water treatment equipment, combining the real-time water quality pollution coefficient and the equipment comprehensive treatment capacity coefficient through a preset water quality effect evaluation model to obtain a water quality treatment effect coefficient; The water quality treatment effect coefficient is compared with a preset water quality treatment effect threshold, and a corresponding water quality treatment adjustment plan is obtained according to the comparison result.

[0037] It should be noted that after the decontamination and purification operation of the target water treatment equipment is completed, it is necessary to judge the decontamination and purification effect of the equipment. Therefore, first, the corresponding capacity level of each module when working is queried through the value level table of the equipment, including the equipment reverse osmosis capacity level, the equipment filtration capacity level and the equipment disinfection capacity level. Then, according to the capacity levels of the above three modules, processing is performed to obtain the equipment comprehensive treatment capacity coefficient. Furthermore, the pollution coefficient corresponding to the water inlet and the water outlet is combined and processed through a preset water quality effect evaluation model to obtain the equipment's water quality treatment effect coefficient. Among them, the water quality effect evaluation model belongs to a neural network model. The initialized water quality effect evaluation model is trained based on a large number of historical effluent water quality pollution coefficients, water inlet water quality pollution coefficients and equipment comprehensive treatment capacity coefficients to obtain a trained water quality effect evaluation model. If the coefficient is less than the preset value, it means that the equipment's water quality treatment effect is not good, and the water treatment plan needs to be readjusted, such as maintaining or upgrading the water treatment equipment.

[0038] In a second aspect, the present invention further discloses a water quality monitoring and intelligent control system based on big data, including a memory and a processor, wherein the memory includes a water quality monitoring and intelligent control method program based on big data, and when the water quality monitoring and intelligent control method program based on big data is executed by the processor, the following steps are implemented: Monitor the water quality composition information at the water inlet of the target water treatment equipment and extract real-time water quality characteristic data; Processing the real-time water quality characteristic data to obtain a real-time water quality pollution coefficient, and determining 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, and process 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; Processing the physical characteristic change rate data to obtain the physical indicator change rate abnormality coefficient, and performing a reverse osmosis module startup determination response; Processing the physical characteristic change rate data and the chemical characteristic change rate data to obtain the abnormal coefficient of the physical and chemical index change rate, and performing a start-up judgment response of the activated carbon filtration module; The biological characteristic change rate data is processed to obtain the abnormal coefficient of the biological indicator change rate, and the ultraviolet disinfection module startup determination response is performed.

[0039] It should be noted that with the acceleration of industrialization and the growth of population, the pollution problem of water resources is becoming increasingly serious. Traditional water quality monitoring methods have problems such as long monitoring cycle, inaccurate data, and inability to monitor in real time, which cannot meet the needs of modern water resource management. At the same time, traditional water quality control methods also have problems such as inaccurate 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 realize intelligent control of water quality. In this embodiment, the water quality component information at the water inlet of the target water treatment equipment is first monitored, and real-time water quality characteristic data is extracted, 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 the real-time water quality pollution coefficient, and it is determined whether the target water treatment equipment needs to be started. If the target water needs to be started, A processing device obtains water quality characteristic data corresponding to a first preset time node and a second preset time node at a water inlet, and processes 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, processes according to the physical characteristic change rate data, obtains the abnormal coefficient of the physical indicator change rate, and makes a start-up judgment response to the reverse osmosis module of the target water treatment equipment, processes according to the physical characteristic change rate data and the chemical characteristic change rate data, obtains the abnormal coefficient of the physicochemical indicator change rate, and makes a start-up judgment response to the activated carbon filtration module of the target water treatment equipment, processes according to the biological characteristic change rate data, obtains the abnormal coefficient of the biological indicator change rate, and makes a start-up judgment response to the ultraviolet disinfection module of the target water treatment equipment, thereby realizing the technology of water quality monitoring and intelligent control based on big data.

[0040] According to an embodiment of the present invention, the monitoring of water quality component information at the water inlet of the target water treatment equipment and extracting real-time water quality characteristic data includes: Real-time monitoring of water quality component information at the water inlet of the target water treatment equipment, including physical component information, chemical component information and biological component information; Extracting real-time water quality characteristic data according to 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; The real-time physical characteristic data includes real-time salt content data, real-time hardness data, real-time heavy metal content data, real-time chromaticity 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 bacteria count data and real-time total coliform count data.

[0041] It should be noted that before monitoring the water quality, it is necessary to understand the composition of the water as much as possible. Therefore, it is necessary to monitor the water quality component information at the water inlet of the target water treatment equipment in real time, including physical composition, chemical composition and biological composition information, and extract the real-time physical characteristic data, real-time chemical characteristic data and real-time biological characteristic data of the water quality according to the above information. Among them, the real-time physical characteristic data include real-time salt content, real-time hardness, real-time heavy metal content, real-time color and real-time odor intensity data, the real-time chemical characteristic data include real-time chemical oxygen demand and real-time biochemical oxygen demand data, and the real-time biological characteristic data include real-time total bacteria count and real-time total coliform count data.

[0042] According to an embodiment of the present invention, the processing according to the real-time water quality characteristic data to obtain the real-time water quality pollution coefficient and determine whether the target water treatment equipment needs to be started includes: 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; Comparing the real-time water pollution coefficient with a preset water pollution threshold to obtain a comparison result; If the real-time water quality pollution coefficient is greater than the preset water quality pollution threshold, the target water treatment equipment is started.

[0043] It should be noted that it is necessary to judge the degree of water pollution. When the water quality is polluted to a certain extent, it is necessary to start the target water treatment equipment to decontaminate and purify the water. Therefore, the real-time water quality characteristic data is processed through a preset water quality pollution detection model to obtain a real-time water quality pollution coefficient. The water quality pollution detection model is a neural network model. The initialized water quality pollution detection model is trained based on a large amount of historical water quality characteristic data to obtain a trained water quality pollution detection model and determine whether the coefficient exceeds the preset value. If it exceeds the preset value, the target water treatment equipment needs to be started.

[0044] According to an embodiment of the present invention, 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, including: If it is necessary to start the target water treatment equipment, obtain water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet; According to 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 respectively 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 physical characteristic change rate data include salt content change rate data, hardness change rate data, heavy metal content change rate data, chromaticity 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 biological characteristic change rate data includes total bacteria count change rate data and total coliform count change rate data.

[0045] It should be noted that if it is determined that the target water treatment equipment needs to be started to purify the water, the changes in the water pollution indicators should be further understood. If the corresponding water quality indicators gradually become more serious over time, it means that they are normally polluted, and the water purification work can be completed by operating according to the original conventional equipment operating parameters. However, if the changes in the water pollution indicators are rapid and sudden, it means that the water quality is polluted by other sources and the pollution is unconventional. Therefore, it is necessary to adjust the parameters of the processing module corresponding to the target water treatment equipment accordingly. The processing modules of the target water treatment equipment include reverse osmosis modules, activated carbon filtration modules and ultraviolet disinfection modules. Among them, the water quality characteristic change rate data include physical characteristic change rate, chemical characteristic change rate and biological characteristic change rate data. The physical characteristic change rate data include salt content change rate, hardness change rate, heavy metal content change rate, chromaticity change rate and odor intensity change rate data. The chemical characteristic change rate data include chemical oxygen demand change rate and biochemical oxygen demand change rate data. The biological characteristic change rate data includes total bacteria change rate and total coliform count change rate data.

[0046] According to an embodiment of the present invention, the processing according to the physical characteristic change rate data to obtain the physical indicator change rate abnormality coefficient and perform a reverse osmosis module startup determination response includes: Processing the salt content change rate data, the hardness change rate data and the heavy metal content change rate data to obtain the abnormal coefficient of the physical index change rate; Comparing the physical indicator change rate abnormality coefficient with a preset physical indicator change rate abnormality threshold to obtain a comparison result; If the physical indicator change rate abnormal coefficient is greater than the preset physical indicator change rate abnormal threshold, 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 operation parameters include water inlet parameters, water production parameters and membrane performance parameters.

[0047] It should be noted that, according to the salt content change rate data, hardness change rate data and heavy metal content change rate data of water quality, the abnormal coefficient of the change rate of the physical index of water quality is obtained. If the abnormal amount of the change rate of the physical index of water quality exceeds the preset threshold, the reverse osmosis operation parameters of the reverse osmosis module are adjusted and the reverse osmosis module is started, wherein the reverse osmosis operation parameters include water inlet parameters, water production parameters and membrane performance parameters; The calculation formula of the abnormal coefficient of the physical index change rate is: ; in, is the abnormal coefficient of the change rate of physical indicators, , , They are the salt content change rate data, hardness change rate data, and heavy metal content change rate data. It is a preset correction coefficient, and its value can be determined through experimental fitting or theoretical derivation according to actual scene requirements.

[0048] According to an embodiment of the present invention, the processing according to the physical characteristic change rate data and the chemical characteristic change rate data to obtain the abnormal coefficient of the physicochemical index change rate and to perform an activated carbon filter module startup determination response includes: According to the chromaticity change rate data and the odor intensity change rate data, combined with the chemical oxygen demand change rate data and the biochemical oxygen demand change rate data, the abnormal coefficient of the physical and chemical index change rate is obtained; Comparing the abnormal coefficient of the physicochemical index change rate with a preset abnormal threshold of the physicochemical index change rate to obtain a comparison result; If the abnormal coefficient of the physicochemical index change rate is greater than the preset physicochemical index change rate abnormal threshold, the activated carbon filter module of the target water treatment equipment is started, and the filtering operation parameters of the activated carbon filter module are adjusted accordingly; The filtering operation parameters include activated carbon characteristic parameters and filtering system parameters.

[0049] It should be noted that, according to the chromaticity change rate data and the odor intensity change rate data of the water quality, combined with the chemical oxygen demand change rate data and the biochemical oxygen demand change rate data, the abnormal coefficient of the change rate of the physical and chemical indicators of the water quality is obtained. If the abnormal amount of the change rate of the physical and chemical indicators of the water quality exceeds the preset threshold, the filtering operation parameters of the activated carbon filtering module are adjusted and the activated carbon filtering module is started, wherein the filtering operation parameters include the activated carbon characteristic parameters and the filtering system parameters; The calculation formula of the abnormal coefficient of the physical and chemical index change rate is: ; in, is the abnormal coefficient of the change rate of physical and chemical indicators, , They are the data of chromaticity change rate and odor intensity change rate, , They are the chemical oxygen demand change rate data and the biochemical oxygen demand change rate data. , , It is a preset correction coefficient, and its value can be determined through experimental fitting or theoretical derivation according to actual scene requirements.

[0050] According to an embodiment of the present invention, it also includes: Processing is performed according to the biological characteristic change rate data to obtain the abnormal coefficient of the biological indicator change rate, and a UV disinfection module start-up determination response is performed, specifically including: Processing the total bacteria count change rate data and the total coliform count change rate data to obtain a biological indicator change rate abnormality coefficient; Comparing the biological indicator change rate abnormality coefficient with a preset biological indicator change rate abnormality threshold to obtain a comparison result; If the biological indicator change rate abnormal coefficient is greater than the preset biological indicator change rate abnormal threshold, the ultraviolet disinfection module of the target water treatment equipment is started, 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; The calculation formula of the abnormal coefficient of the biological indicator change rate is: ; in, is the abnormal coefficient of biological index change rate, , They are the total bacterial count change rate data and the total coliform count change rate data. It is a preset correction coefficient, and its value can be determined through experimental fitting or theoretical derivation according to actual scene requirements.

[0051] It should be noted that the water quality biological indicator change rate abnormality coefficient is obtained by processing the total bacteria count change rate data and the total coliform count change rate data. If the abnormal amount of the water quality biological indicator change rate exceeds the preset threshold, the disinfection operation parameters of the active ultraviolet disinfection module are adjusted and the ultraviolet disinfection module is started, wherein the disinfection operation parameters include ultraviolet intensity parameters, water flow parameters and lamp parameters.

[0052] According to an embodiment of the present invention, it also includes: According to the water inlet parameters, water production parameters and membrane performance parameters, a preset water treatment equipment value level table is queried to obtain the equipment reverse osmosis capacity level; According to the activated carbon characteristic parameters and the filtration system parameters, a preset water treatment equipment value level table is queried to obtain the equipment filtration capacity level; According to the ultraviolet intensity parameter, water flow parameter and lamp tube parameter, a preset water treatment equipment value level table is queried to obtain the equipment disinfection capacity level; Perform weighted processing according to the equipment's reverse osmosis capacity level, equipment's filtration capacity level, and equipment's disinfection capacity level to obtain the equipment's comprehensive processing capacity coefficient; Obtaining the water quality pollution coefficient of the outlet water at the outlet of the target water treatment equipment, combining the real-time water quality pollution coefficient and the equipment comprehensive treatment capacity coefficient through a preset water quality effect evaluation model to obtain a water quality treatment effect coefficient; The water quality treatment effect coefficient is compared with a preset water quality treatment effect threshold, and a corresponding water quality treatment adjustment plan is obtained according to the comparison result.

[0053] It should be noted that after the decontamination and purification operation of the target water treatment equipment is completed, it is necessary to judge the decontamination and purification effect of the equipment. Therefore, first, the corresponding capacity level of each module when working is queried through the value level table of the equipment, including the equipment reverse osmosis capacity level, the equipment filtration capacity level and the equipment disinfection capacity level. Then, according to the capacity levels of the above three modules, processing is performed to obtain the equipment comprehensive treatment capacity coefficient. Furthermore, the pollution coefficient corresponding to the water inlet and the water outlet is combined and processed through a preset water quality effect evaluation model to obtain the equipment's water quality treatment effect coefficient. Among them, the water quality effect evaluation model belongs to a neural network model. The initialized water quality effect evaluation model is trained based on a large number of historical effluent water quality pollution coefficients, water inlet water quality pollution coefficients and equipment comprehensive treatment capacity coefficients to obtain a trained water quality effect evaluation model. If the coefficient is less than the preset value, it means that the equipment's water quality treatment effect is not good, and the water treatment plan needs to be readjusted, such as maintaining or upgrading the water treatment equipment.

[0054] The third aspect of the present invention provides a readable storage medium, which stores a water quality monitoring and intelligent control method program based on big data. When the water quality monitoring and intelligent control method program based on big data is executed by a processor, the steps of the water quality monitoring and intelligent control method based on big data as described in any one of the above items are implemented.

[0055] The water quality monitoring and intelligent control method, system and medium based on big data disclosed in the present invention monitor the water quality component information at the water 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, process the real-time water quality characteristic data to obtain the real-time water quality pollution coefficient, and judge whether it is necessary to start the target water treatment equipment. 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 obtained, and the corresponding water quality characteristic change rate data are obtained by processing, including the physical characteristic change rate data, the chemical characteristic data and the biological characteristic data. The data of change rate of physical characteristics and the data of change rate of biological characteristics are processed according to the data of change rate of physical characteristics to obtain the abnormal coefficient of change rate of physical indicators, and the reverse osmosis module of the target water treatment equipment is started and judged in response. The data of change rate of physical characteristics and the data of change rate of chemical characteristics are processed according to the abnormal coefficient of change rate of physicochemical indicators, and the activated carbon filtration module of the target water treatment equipment is started and judged in response. The data of change rate of biological characteristics are processed to obtain the abnormal coefficient of change rate of biological indicators, and the ultraviolet disinfection module of the target water treatment equipment is started and judged in response, thereby realizing the technology of water quality monitoring and intelligent control based on big data.

[0056] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0057] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0058] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0059] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0060] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, 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: The following steps are involved: Monitor the water quality composition information at the water inlet of the target water treatment equipment and extract real-time water quality characteristic data; Processing the real-time water quality characteristic data to obtain a real-time water quality pollution coefficient, and determining 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, and process 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; Processing the physical characteristic change rate data to obtain the physical indicator change rate abnormality coefficient, and performing a reverse osmosis module startup determination response; Processing is performed according to the physical characteristic change rate data and the chemical characteristic change rate data to obtain the abnormal coefficient of the physical and chemical index change rate, and performing a start-up judgment response of the activated carbon filtration module; The biological characteristic change rate data is processed to obtain the abnormal coefficient of the biological indicator change rate, and the ultraviolet disinfection module startup determination response is performed.

2. The water quality monitoring and intelligent control method based on big data according to claim 1 is characterized in that: The monitoring of water quality component information at the water inlet of the target water treatment equipment and extraction of real-time water quality characteristic data include: Real-time monitoring of water quality component information at the water inlet of the target water treatment equipment, including physical component information, chemical component information and biological component information; Extracting real-time water quality characteristic data according to 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; The real-time physical characteristic data includes real-time salt content data, real-time hardness data, real-time heavy metal content data, real-time chromaticity 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 bacteria 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 is characterized in that: The processing according to the real-time water quality characteristic data to obtain the real-time water quality pollution coefficient and determine whether the target water treatment equipment needs to be started includes: 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; Comparing the real-time water pollution coefficient with a preset water pollution threshold to obtain a comparison result; If the real-time water quality pollution coefficient is greater than the preset water quality pollution threshold, the target water treatment equipment is started.

4. The water quality monitoring and intelligent control method based on big data according to claim 3 is characterized in that: 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, including: If it is necessary to start the target water treatment equipment, obtain water quality characteristic data corresponding to the first preset time node and the second preset time node at the water inlet; According to 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 respectively 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 physical characteristic change rate data include salt content change rate data, hardness change rate data, heavy metal content change rate data, chromaticity 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 biological characteristic change rate data includes total bacteria count change rate data and total coliform count change rate data.

5. The water quality monitoring and intelligent control method based on big data according to claim 4 is characterized in that: The processing according to the physical characteristic change rate data to obtain the physical indicator change rate abnormality coefficient and perform reverse osmosis module startup determination response includes: Processing the salt content change rate data, the hardness change rate data and the heavy metal content change rate data to obtain the abnormal coefficient of the physical index change rate; Comparing the physical indicator change rate abnormality coefficient with a preset physical indicator change rate abnormality threshold to obtain a comparison result; If the physical indicator change rate abnormal coefficient is greater than the preset physical indicator change rate abnormal threshold, 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 operation parameters include water inlet parameters, water production parameters and membrane performance parameters.

6. The water quality monitoring and intelligent control method based on big data according to claim 5 is characterized in that: The processing according to the physical characteristic change rate data and the chemical characteristic change rate data to obtain the abnormal coefficient of the physical and chemical index change rate and to perform the activated carbon filter module start-up determination response includes: According to the chromaticity change rate data and the odor intensity change rate data, combined with the chemical oxygen demand change rate data and the biochemical oxygen demand change rate data, the abnormal coefficient of the physical and chemical index change rate is obtained; Comparing the abnormal coefficient of the physicochemical index change rate with a preset abnormal threshold of the physicochemical index change rate to obtain a comparison result; If the abnormal coefficient of the physicochemical index change rate is greater than the preset physicochemical index change rate abnormal threshold, the activated carbon filter module of the target water treatment equipment is started, and the filtering operation parameters of the activated carbon filter module are adjusted accordingly; The filtering operation parameters include activated carbon characteristic parameters and filtering system parameters.

7. A water quality monitoring and intelligent control system based on big data, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of a water quality monitoring and intelligent control method based on big data, and when the program of the water quality monitoring and intelligent control method based on big data is executed by the processor, the following steps are implemented: Monitor the water quality composition information at the water inlet of the target water treatment equipment and extract real-time water quality characteristic data; Processing the real-time water quality characteristic data to obtain a real-time water quality pollution coefficient, and determining 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, and process 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; Processing the physical characteristic change rate data to obtain the physical indicator change rate abnormality coefficient, and performing a reverse osmosis module startup determination response; Processing is performed according to the physical characteristic change rate data and the chemical characteristic change rate data to obtain the abnormal coefficient of the physical and chemical index change rate, and performing a start-up judgment response of the activated carbon filtration module; The biological characteristic change rate data is processed to obtain the abnormal coefficient of the biological indicator change rate, and the ultraviolet disinfection module startup determination response is performed.

8. The water quality monitoring and intelligent control system based on big data according to claim 7 is characterized in that: The monitoring of water quality component information at the water inlet of the target water treatment equipment and extraction of real-time water quality characteristic data include: Real-time monitoring of water quality component information at the water inlet of the target water treatment equipment, including physical component information, chemical component information and biological component information; Extracting real-time water quality characteristic data according to 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; The real-time physical characteristic data includes real-time salt content data, real-time hardness data, real-time heavy metal content data, real-time chromaticity 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 bacteria count data and real-time total coliform count data.

9. The water quality monitoring and intelligent control system based on big data according to claim 8 is characterized in that: The processing according to the real-time water quality characteristic data to obtain the real-time water quality pollution coefficient and determine whether the target water treatment equipment needs to be started includes: 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; Comparing the real-time water pollution coefficient with a preset water pollution threshold to obtain a comparison result; If the real-time water quality pollution coefficient is greater than the preset water quality pollution threshold, the target water treatment equipment is started.

10. 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 water quality monitoring and intelligent control method program based on big data is executed by a processor, the steps of the water quality monitoring and intelligent control method based on big data as described in any one of claims 1 to 6 are implemented.

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