Water quality monitoring analysis method and system of water conservancy system

Abstract

The invention relates to the technical field of water quality monitoring, in particular to a water quality monitoring analysis method and system of a water conservancy system. The method comprises the following steps: acquiring area scale and water area type distribution, and carrying out sensing planning layout and water quality index real-time monitoring to obtain a water conservancy system water quality index data set; performing regional water quality index distribution coupling and regional time step division on the water conservancy system to obtain a corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration and water conservancy microorganism concentration; performing microorganism growth efficiency evaluation and microorganism pollution attenuation analysis on the water conservancy microorganism concentration to obtain a water conservancy area microorganism pollution degradation attenuation rate; and performing water quality pollution anomaly quantification and anomaly category decision management on the water quality index data set of the water conservancy system to generate a water quality anomaly category management strategy so as to execute corresponding regional water quality category management work of the water conservancy system. The accuracy and efficiency of water quality monitoring can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring, and particularly to a water quality monitoring and analysis method and system for a water conservancy system. Background Art

[0002] In recent years, water quality monitoring systems based on automated sensors have gradually been applied. Such systems can monitor multiple water quality indicators of water bodies in real time and continuously, greatly improving the efficiency and accuracy of water quality monitoring. By combining multi-dimensional sensor data with deep learning technology, and adopting intelligent data fusion and real-time analysis methods, a comprehensive analysis of water quality changes is carried out. By establishing correlation models between multiple water quality indicators, this method can effectively identify the laws of water quality changes, monitor the water body pollution status in real time, and predict the trend of water quality changes based on historical data and real-time monitoring data, providing a scientific basis for pollution warning and decision-making in the water conservancy system. However, traditional water quality monitoring methods mainly include three means: physical, chemical, and biological monitoring. Among them, physical monitoring mainly detects through indicators such as water temperature and turbidity, chemical monitoring detects the concentrations of pollutants such as dissolved oxygen, ammonia nitrogen, and total phosphorus in water, and biological monitoring evaluates the ecological health of water bodies through changes in biological indicator species. These traditional methods usually rely on processes such as manual sampling and chemical analysis, which are not only time-consuming and laborious, but also cannot achieve real-time monitoring to a certain extent, thus affecting the accuracy and reliability of monitoring results. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a water quality monitoring and analysis method and system for a water conservancy system to solve at least one of the above technical problems.

[0004] To achieve the above object, a water quality monitoring and analysis method for a water conservancy system includes the following steps:

[0005] Step S1: Obtain the regional scale and water area type distribution corresponding to the water conservancy system, and conduct a sensing planning layout for the water conservancy system based on the regional scale and water area type distribution to generate water conservancy system sensing planning layout nodes; deploy corresponding water quality sensors inside the water conservancy system sensing planning layout nodes to form a distributed IoT sensing network, and use the distributed IoT sensing network to conduct real-time monitoring of water quality indicators of the water conservancy system to obtain a water conservancy system water quality indicator dataset, which includes the pH value of the water conservancy area, the dissolved oxygen concentration of the water conservancy area, the turbidity of the water conservancy area, the heavy metal concentration of the water conservancy area, and the microorganism concentration of the water conservancy area;

[0006] Step S2: Based on the water quality index dataset of the water conservancy system, perform regional water quality index distribution coupling on the water conservancy system to generate a regional water quality index distribution field of the water conservancy system; perform regional time step division on the regional water quality index distribution field of the water conservancy system to obtain the corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration, and water conservancy microorganism concentration in each time step of the water conservancy system region;

[0007] Step S3: Based on the water conservancy pH value, water conservancy dissolved oxygen concentration, and water conservancy heavy metal concentration corresponding to each time step in the water conservancy system region, evaluate the microbial growth efficiency of the water conservancy microorganism concentration corresponding to each time step in the water conservancy system region to obtain the microbial growth influence efficiency in the water conservancy region; obtain the water body pollution distribution area corresponding to each time step in the water conservancy system region, and perform microbial pollution attenuation analysis on the corresponding water conservancy microorganism concentration based on the water body pollution distribution area and water conservancy turbidity corresponding to each time step in the water conservancy system region to obtain the microbial pollution degradation attenuation rate in the water conservancy region;

[0008] Step S4: Based on the microbial growth influence efficiency in the water conservancy region and the microbial pollution degradation attenuation rate in the water conservancy region, quantify the water quality pollution anomaly of the water quality index dataset of the water conservancy system to obtain the water quality pollution anomaly degree in the water conservancy system region; based on the water quality pollution anomaly degree in the water conservancy system region, perform abnormal category decision management on the corresponding regional water quality in the water conservancy system, generate a water quality abnormal category management strategy corresponding to the water conservancy system region, and execute the corresponding water quality category management work in the water conservancy system region.

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

[0010] Step S11: Obtain the regional scale and water area type distribution corresponding to the water conservancy system;

[0011] Step S12: Based on the regional scale and water area type distribution corresponding to the water conservancy system, perform regional hydrodynamic sensitivity gradient analysis on the water conservancy system, and use computational fluid dynamics (CFD) to simulate the flow velocity, pressure, and turbulence intensity of the water conservancy system under different terrain scales and water area distributions to analyze the corresponding regional hydrodynamic sensitivity gradient distribution, and generate a regional hydrodynamic sensitivity gradient distribution field of the water conservancy system;

[0012] Step S13: Perform sensing planning and layout on the regional hydrodynamic sensitivity gradient distribution field of the water conservancy system, and set a comprehensive sensing and monitoring node every 5 - 10 kilometers at the corresponding medium and high gradient distribution areas in the regional hydrodynamic sensitivity gradient distribution field of the water conservancy system to generate the sensing planning and layout nodes of the water conservancy system;

[0013] Step S14: Deploy corresponding pH sensors, dissolved oxygen sensors, turbidity sensors, heavy metal sensors, and microbial monitoring sensors inside each sensing and planning node of the water conservancy system and connect them to generate a distributed IoT sensing network;

[0014] Step S15: Use the distributed IoT sensing network to monitor the water quality indicators of the water conservancy system in real time to obtain a water quality indicator dataset of the water conservancy system, including the pH value of the water conservancy area, the dissolved oxygen concentration of the water conservancy area, the turbidity of the water conservancy area, the heavy metal concentration of the water conservancy area, and the microbial concentration of the water conservancy area.

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

[0016] Step S21: Obtain the water conservancy geographical spatial location distribution and the water conservancy topographic and geomorphic elevation distribution corresponding to the water conservancy system;

[0017] Step S22: Conduct a water conservancy spatial distribution simulation on the water conservancy system based on the water conservancy geographical spatial location distribution and the water conservancy topographic and geomorphic elevation distribution to generate a regional spatial simulation distribution field of the water conservancy system;

[0018] Step S23: Conduct a spatio-temporal scale fluctuation analysis on each water quality indicator in the water quality indicator dataset of the water conservancy system to obtain the spatio-temporal scale change fluctuation distribution corresponding to each water quality indicator of the water conservancy system;

[0019] Step S24: Conduct a regional water quality indicator distribution coupling on the regional spatial simulation distribution field of the water conservancy system based on the spatio-temporal scale change fluctuation distribution corresponding to each water quality indicator of the water conservancy system to generate a regional water quality indicator distribution field of the water conservancy system;

[0020] Step S25: Conduct a regional time step division on the regional water quality indicator distribution field of the water conservancy system to obtain the water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration, and water conservancy microbial concentration corresponding to each time step of the water conservancy system area.

[0021] Further, step S3 includes the following steps:

[0022] Step S31: Obtain the corresponding water conservancy microbial growth and reproduction amount through the water conservancy microbial concentration corresponding to each time step of the water conservancy system area;

[0023] Step S32: Calculate the growth rate of the water conservancy microbial growth and reproduction amount corresponding to each time step of the water conservancy system area to obtain the water conservancy microbial growth rate corresponding to each time step of the water conservancy system area.

[0024] Step S33: Based on the water conservancy pH value, water conservancy dissolved oxygen concentration, and water conservancy heavy metal concentration corresponding to the water conservancy system area at each time step, evaluate the microbial growth efficiency of the water conservancy microbial growth rate corresponding to the water conservancy system area at each time step to obtain the water conservancy area microbial growth influence efficiency;

[0025] Step S34: Obtain the water body suspended solid distribution corresponding to the water conservancy system area at each time step, and quantify the pollution distribution according to the water body suspended solid distribution corresponding to the water conservancy system area at each time step to obtain the water body pollution distribution area corresponding to the water conservancy system area at each time step;

[0026] Step S35: Based on the water body pollution distribution area and water conservancy turbidity corresponding to the water conservancy system area at each time step, conduct microbial pollution attenuation analysis on the corresponding water conservancy microbial concentration to obtain the water conservancy area microbial pollution degradation attenuation rate.

[0027] Further, step S33 includes the following steps:

[0028] Based on the water conservancy pH value and water conservancy dissolved oxygen concentration corresponding to the water conservancy system area at each time step, conduct heavy metal oxidation-reduction influence analysis on the corresponding water conservancy heavy metal concentration to obtain the heavy metal oxidation-reduction state influence coefficient corresponding to the water conservancy system area at each time step;

[0029] According to the heavy metal oxidation-reduction state influence coefficient corresponding to the water conservancy system area at each time step, conduct growth substrate heavy metal affinity analysis on the corresponding microbial growth process within the water conservancy system area to obtain the microbial growth substrate heavy metal affinity constant corresponding to the water conservancy system area at each time step;

[0030] Based on the water conservancy pH value, water conservancy dissolved oxygen concentration, and microbial growth substrate heavy metal affinity constant corresponding to the water conservancy system area at each time step, use the microbial growth influence calculation formula to evaluate the microbial growth efficiency of the water conservancy microbial growth rate corresponding to the water conservancy system area at each time step to obtain the water conservancy area microbial growth influence efficiency.

[0031] Further, the specific microbial growth influence calculation formula is:

[0032]

[0033] In the formula, μ s is the water conservancy area microbial growth influence efficiency, n is the total number corresponding to the time step, i is the item index corresponding to the time step, T iis the i-th time step, t is the time step variable parameter, μ(t) is the growth rate of water conservancy microorganisms corresponding to the water conservancy system area at time step t, S(t) is the substrate concentration of water conservancy microorganisms corresponding to the water conservancy system area at time step t, K S is the half-saturation constant of water conservancy microorganism substrate, O(t) is the dissolved oxygen concentration of water conservancy corresponding to the water conservancy system area at time step t, K O is the half-saturation constant of water conservancy dissolved oxygen, exp is the exponential function, pH(t) is the water conservancy pH value corresponding to the water conservancy system area at time step t, α is the microorganism growth inhibition coefficient, M(t) is the heavy metal concentration corresponding to the water conservancy system area at time step t, K M is the half-saturation constant of heavy metals, ε is the heavy metal affinity constant of microorganism growth substrate, and η is the correction coefficient of the influence efficiency of microorganism growth in the water conservancy area.

[0034] Further, step S35 includes the following steps:

[0035] Step S351: Based on the water pollution distribution area and water conservancy turbidity corresponding to the water conservancy system area at each time step, perform microbial pollution distribution attenuation simulation on the corresponding water conservancy microorganism concentration to generate the microbial pollution distribution attenuation process corresponding to the water conservancy system area at each time step;

[0036] Step S352: Determine the decay half-life of the microbial pollution distribution attenuation process corresponding to the water conservancy system area at each time step to obtain the microbial pollution decay half-life corresponding to the water conservancy system area at each time step;

[0037] Step S353: Based on the microbial pollution decay half-life corresponding to the water conservancy system area at each time step, quantify the water pollution distribution in the microbial pollution distribution attenuation process to obtain the microbial water pollution degradation attenuation amount corresponding to the water conservancy system area at each time step;

[0038] Step S354: Calculate the attenuation rate according to the microbial water pollution degradation attenuation amount corresponding to the water conservancy system area at each time step to obtain the microbial pollution degradation attenuation rate in the water conservancy area.

[0039] Further, step S4 includes the following steps:

[0040] Step S41: Based on the influence efficiency of microorganism growth in the water conservancy area and the microbial pollution degradation attenuation rate in the water conservancy area, use the regional water quality pollution anomaly calculation formula to quantify the water quality pollution anomaly of the water conservancy system water quality index dataset to obtain the degree of water quality pollution anomaly in the water conservancy system area;

[0041] Step S42: Based on the abnormal degree of water quality pollution in the water conservancy system area, determine and classify the corresponding regional water quality in the water conservancy system to generate the corresponding water quality abnormal categories for the water conservancy system area, including water quality abnormality Class I, water quality abnormality Class II, and water quality abnormality Class III;

[0042] Step S43: According to the corresponding water quality abnormal categories of the water conservancy system area, conduct decision-making management on the corresponding regional water quality in the water conservancy system to generate the corresponding water quality abnormal category management strategies for the water conservancy system area, so as to perform the corresponding water quality category management work for the water conservancy system area.

[0043] Further, the specific formula for the abnormal calculation of regional water quality pollution in Step S41 is as follows:

[0044]

[0045] In the formula, P is the abnormal degree of water quality pollution in the water conservancy system area, A is the size of the water conservancy system area, x is the abscissa of the location point of the water conservancy system area, y is the ordinate of the location point of the water conservancy system area, pH(x, y) is the water conservancy pH value at the location point (x, y) in the water conservancy system area, O(x, y) is the water conservancy dissolved oxygen concentration at the location point (x, y) in the water conservancy system area, Z(x, y) is the water conservancy turbidity at the location point (x, y) in the water conservancy system area, M(x, y) is the water conservancy heavy metal concentration at the location point (x, y) in the water conservancy system area, C(x, y) is the water conservancy microorganism concentration at the location point (x, y) in the water conservancy system area, μ s is the influence efficiency of microorganism growth in the water conservancy area, δ v is the degradation attenuation rate of microorganism pollution in the water conservancy area, and ξ is the correction coefficient of the abnormal degree of water quality pollution in the water conservancy system area.

[0046] Further, the present invention also provides a water quality monitoring and analysis system for a water conservancy system, which is used to execute the water quality monitoring and analysis method of the water conservancy system as described above. The water quality monitoring and analysis system for the water conservancy system includes:

[0047] The water quality index monitoring module of the water conservancy system is used to obtain the corresponding regional scale and water area type distribution of the water conservancy system, and conduct sensing planning and layout on the water conservancy system based on the regional scale and water area type distribution to generate the sensing planning and layout nodes of the water conservancy system; deploy the corresponding water quality sensors inside the sensing planning and layout nodes of the water conservancy system to form a distributed IoT sensing network, and use the distributed IoT sensing network to conduct real-time monitoring of the water quality indexes of the water conservancy system, so as to obtain the water quality index data set of the water conservancy system, including the water conservancy area pH value, water conservancy area dissolved oxygen concentration, water conservancy area turbidity, water conservancy area heavy metal concentration, and water conservancy area microorganism concentration;

[0048] The regional index time-step division module is used to couple the regional water quality index distribution of the water conservancy system based on the water quality index data set of the water conservancy system to generate the regional water quality index distribution field of the water conservancy system; perform regional time-step division on the regional water quality index distribution field of the water conservancy system, so as to obtain the corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration, and water conservancy microorganism concentration in each time step of the water conservancy system region;

[0049] The regional microorganism water quality impact analysis module is used to evaluate the microbial growth efficiency of the water conservancy microorganism concentration corresponding to each time step in the water conservancy system region based on the water conservancy pH value, water conservancy dissolved oxygen concentration, and water conservancy heavy metal concentration corresponding to each time step in the water conservancy system region, so as to obtain the water conservancy regional microbial growth impact efficiency; obtain the water body pollution distribution area corresponding to each time step in the water conservancy system region, and perform microbial pollution attenuation analysis on the corresponding water conservancy microorganism concentration based on the water body pollution distribution area corresponding to each time step in the water conservancy system region and the water conservancy turbidity, so as to obtain the water conservancy regional microbial pollution degradation attenuation rate;

[0050] The regional water quality pollution anomaly management module is used to quantify the water quality pollution anomaly of the water quality index data set of the water conservancy system based on the water conservancy regional microbial growth impact efficiency and the water conservancy regional microbial pollution degradation attenuation rate, so as to obtain the water quality pollution anomaly degree of the water conservancy system region; perform abnormal category decision management on the corresponding regional water quality in the water conservancy system based on the water quality pollution anomaly degree of the water conservancy system region, generate the water quality anomaly category management strategy corresponding to the water conservancy system region, so as to execute the corresponding water conservancy system regional water quality category management work.

[0051] The beneficial effects of the present invention:

[0052] 1. Compared with the prior art, the beneficial effect of the water quality monitoring and analysis method of the water conservancy system proposed by the present invention is that by obtaining the regional scale and water area type distribution corresponding to the water conservancy system, it can provide necessary basic data for the sensing planning of the water conservancy system. The significance of this process lies in that by analyzing the regional scale and water area type (such as rivers, lakes, reservoir areas, etc.) of the water area in detail, it can ensure that the positions of sensor deployment are both representative and can cover the key areas of the water conservancy system, thus ensuring the comprehensiveness and accuracy of water quality monitoring data. The reasonable distribution of sensor deployment nodes can maximize the efficiency of data collection and avoid the occurrence of local monitoring blind spots. At the same time, by deploying water quality sensors on these nodes, a distributed Internet of Things sensing network is generated. This network can not only collect water quality data in real time but also maintain a stable monitoring state under various environmental conditions. Based on the construction of the distributed Internet of Things sensing network, water quality sensors can monitor multiple key water quality indicators in the water area in real time, such as pH value, dissolved oxygen concentration, turbidity, heavy metal concentration, and microorganism concentration, etc., which can help managers detect water quality changes in a timely manner, quickly respond to potential water quality pollution problems, prevent the destruction of the water area ecological environment, enhance the timeliness and comprehensiveness of data, and provide accurate information support for water quality management decisions. Secondly, through the distribution coupling of regional water quality indicators based on the water quality indicator data set, the correlation analysis between different water quality indicators can be realized, and the mutual influence of water quality changes can be deeply understood. The coupling analysis can reveal the potential relationship between different water quality indicators. For example, the pH value and dissolved oxygen concentration of the water body affect the microbial community in the water body, while the turbidity and heavy metal concentration of the water body affect the growth and reproduction of microorganisms. Through the analysis of this coupling relationship, the impact of different factors on the health of the water body can be predicted more accurately. Also, by dividing the water quality indicator distribution field into regional time steps, the water quality data can be detailedly segmented by time to facilitate the observation of the dynamic changes of water quality at different time steps. The importance of this division process lies in that it can help the water conservancy management department identify the trends and periodic characteristics of water quality changes and provide support for formulating reasonable water quality management strategies, thereby improving the scientificity and accuracy of water quality management. Then, through the evaluation of the microbial growth efficiency and the analysis of water body pollution attenuation based on the water quality data at different time steps within the water conservancy system region, the microbial community in the water body will undergo complex growth and attenuation processes under the action of water quality changes. Through the comprehensive analysis of water quality indicators (such as pH value, dissolved oxygen concentration, heavy metal concentration, etc.), the growth efficiency of microorganisms in the water body can be evaluated, and then the ecological health of the water body can be scientifically evaluated. For example, the changes in pH value and dissolved oxygen concentration affect the growth of beneficial microorganisms in the water body, while the increase in heavy metal concentration inhibits the activity of microorganisms. By evaluating the growth efficiency of microorganisms, it can help managers understand the potential risks of water body pollution and take timely measures to prevent the further deterioration of the ecosystem.Moreover, by combining the water pollution distribution area and water quality turbidity, the role played by microorganisms in the water pollution process can be quantified. Areas with higher water turbidity usually indicate the accumulation and spread of pollutants, and the change in microorganism concentration can directly affect the self-purification ability of water quality. By analyzing the attenuation rate of microorganism pollution, data support can be provided for water quality treatment, enabling in-depth analysis of the relationship between water quality pollution and microorganism growth. This step, by evaluating the growth efficiency of microorganisms and pollution attenuation, can provide crucial data support for subsequent treatment processes. Finally, by anomalously quantifying water quality data based on microorganism growth efficiency and pollution attenuation rate, it can help managers monitor the abnormal conditions of water quality pollution in real time and take necessary countermeasures promptly. This process can help the water conservancy department quickly identify existing pollution hotspots by quantifying and analyzing the abnormal degree of water quality pollution. This anomalous quantification can not only provide an accurate assessment of the pollution level but also reveal the pollution source through data analysis, helping managers control water pollution at the source. In addition, the formulation of the water quality anomaly category management strategy enables the water conservancy system to adopt differentiated countermeasures according to different pollution types (such as heavy metal pollution, organic pollution, etc.). For example, chemical precipitation methods need to be used for heavy metal pollution treatment, while biological remediation is required for organic pollution. By establishing the water quality anomaly category management strategy, the efficiency and accuracy of water quality treatment can be improved, unnecessary resource waste can be reduced, accurate quantification and classification management of water quality pollution can be achieved, helping the water conservancy department promptly discover water quality anomalies and formulate corresponding countermeasures. The work in this stage not only improves the efficiency of water quality monitoring and treatment but also provides a guarantee for the sustainable utilization of water resources, enabling more scientific, accurate, and efficient water quality monitoring and treatment, and improving the accuracy and reliability of water quality monitoring in the water conservancy system.

[0053] 2. The water quality monitoring and analysis system of the water conservancy system proposed by the present invention is generally composed of a water conservancy system water quality index monitoring module, a regional index time step division module, a regional microorganism water quality impact analysis module, and a regional water quality pollution anomaly management module, and can implement the water quality monitoring and analysis method of any water conservancy system described in the present invention. It is used to realize the water quality monitoring and analysis method of the water conservancy system through the operation of computer programs on each module. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower input, quickly and effectively provide a more accurate and efficient water quality monitoring and analysis process for the water conservancy system, thereby simplifying the operation process of the water quality monitoring and analysis system of the water conservancy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0055] Figure 1 Schematic diagram of the step process of the water quality monitoring and analysis method for the water conservancy system of the present invention;

[0056] Figure 2 is Figure 1 detailed step process schematic diagram of step S1 in;

[0057] Figure 3 is Figure 1 detailed step process schematic diagram of step S2 in. Specific implementation manner

[0058] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work belong to the scope of protection of the present invention.

[0059] To achieve the above object, please refer to Figures 1 to 2 , the present invention provides a water quality monitoring and analysis method for a water conservancy system, and the method includes the following steps:

[0060] Step S1: Obtain the regional scale and water area type distribution corresponding to the water conservancy system, and based on the regional scale and water area type distribution, conduct a sensing planning layout for the water conservancy system to generate water conservancy system sensing planning layout nodes; deploy corresponding water quality sensors inside the water conservancy system sensing planning layout nodes to connect and generate a distributed IoT sensing network, and use the distributed IoT sensing network to conduct real-time monitoring of the water quality indicators of the water conservancy system to obtain a water conservancy system water quality indicator dataset, including the pH value of the water conservancy area, the dissolved oxygen concentration of the water conservancy area, the turbidity of the water conservancy area, the heavy metal concentration of the water conservancy area, and the microorganism concentration of the water conservancy area;

[0061] Step S2: Based on the water conservancy system water quality indicator dataset, conduct a coupling of the regional water quality indicator distribution of the water conservancy system to generate a water conservancy system regional water quality indicator distribution field; conduct a regional time step division on the water conservancy system regional water quality indicator distribution field to obtain the corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration, and water conservancy microorganism concentration of the water conservancy system region at each time step;

[0062] Step S3: Based on the water conservancy pH value, water conservancy dissolved oxygen concentration, and water conservancy heavy metal concentration corresponding to the water conservancy system area at each time step, evaluate the microbial growth efficiency of the water conservancy microbial concentration corresponding to the water conservancy system area at each time step to obtain the water conservancy area microbial growth influence efficiency; obtain the water body pollution distribution area corresponding to the water conservancy system area at each time step, and based on the water body pollution distribution area and water conservancy turbidity corresponding to the water conservancy system area at each time step, conduct microbial pollution attenuation analysis on the corresponding water conservancy microbial concentration to obtain the water conservancy area microbial pollution degradation attenuation rate;

[0063] Step S4: Quantify the water quality pollution anomaly of the water conservancy system water quality index dataset based on the water conservancy area microbial growth influence efficiency and the water conservancy area microbial pollution degradation attenuation rate to obtain the water quality pollution anomaly degree of the water conservancy system area; make an abnormal category decision management for the corresponding regional water quality in the water conservancy system based on the water quality pollution anomaly degree of the water conservancy system area, generate a water quality anomaly category management strategy corresponding to the water conservancy system area, and execute the corresponding water conservancy system area water quality category management work.

[0064] In the embodiment of the present invention, please refer to Figure 1 as shown, which is a schematic diagram of the step flow of the water quality monitoring and analysis method of the water conservancy system of the present invention. In this example, the water quality monitoring and analysis method of the water conservancy system includes the following steps:

[0065] Step S1: Obtain the regional scale and water area type distribution corresponding to the water conservancy system, and based on the regional scale and water area type distribution, conduct sensing planning and layout for the water conservancy system to generate water conservancy system sensing planning and layout nodes; deploy corresponding water quality sensors inside the water conservancy system sensing planning and layout nodes to form a distributed IoT sensing network, and use the distributed IoT sensing network to conduct real-time monitoring of the water quality indicators of the water conservancy system to obtain the water conservancy system water quality index dataset, which includes the water conservancy area pH value, water conservancy area dissolved oxygen concentration, water conservancy area turbidity, water conservancy area heavy metal concentration, and water conservancy area microbial concentration;

[0066] In the embodiments of the present invention, by obtaining the geographical information data of the water conservancy system, the area scale covered by the water conservancy system is obtained, including the geographical location, area, and water area type of each sub-region. The water area type can be divided into types such as lakes, rivers, reservoirs, wetlands, etc. Using remote sensing technology and GIS (Geographic Information System) technology, through high-precision satellite images and water area distribution maps, the spatial location and type information of each water area are identified. Then, according to the characteristics of different water area types, for example, the river area has a strong water flow velocity, the lake area has relatively stable water bodies, and there are factors such as waterweed growth in the wetland area, a corresponding sensor layout strategy is formulated. During the sensing planning and layout process of the water conservancy system, according to the area, characteristics, and water area type of the region, the number and distribution method of the sensors to be laid are reasonably selected. For example, in the river area with fast water flow, a relatively dense sensor layout can be selected, while in the lake and reservoir areas, the types of sensors can be appropriately increased. The selection of sensors includes water quality sensors, temperature sensors, dissolved oxygen sensors, heavy metal sensors, etc. Through sensing planning and design, it is ensured to cover the water quality changes of the entire water conservancy system. After the sensors are laid, a distributed IoT sensing network is established using wireless communication technologies (such as LoRa, NB-IoT). The nodes of the sensor network collect and upload water quality data in real time through data transmission methods, and ensure the reliability and real-time nature of the data. At this time, all water quality sensors work together to monitor the water quality indicators of each water area in real time, such as pH value, dissolved oxygen concentration, turbidity, heavy metal concentration, and microbial concentration, and finally obtain a water quality indicator dataset of the water conservancy system.

[0067] Step S2: Based on the water quality indicator dataset of the water conservancy system, perform regional water quality indicator distribution coupling on the water conservancy system to generate a regional water quality indicator distribution field of the water conservancy system; perform regional time step division on the regional water quality indicator distribution field of the water conservancy system to obtain the corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration, and water conservancy microbial concentration of the water conservancy system region at each time step;

[0068] In an embodiment of the present invention, by combining the water quality data collected by sensors, a coupled analysis of the water quality indicators in each region is performed in terms of time and space. By integrating the water quality data of different regions at different time steps, a distribution field of water quality indicators for each region is formed. For example, for a certain moment or time period, data such as pH value, dissolved oxygen concentration, turbidity, and heavy metal concentration in a specific region are extracted and integrated into a unified distribution field, thereby generating a distribution field of water quality indicators for the water conservancy system region. Then, according to actual needs, the water quality indicator data is divided into time steps. This can be achieved by using a time series analysis method for continuous data streams and performing step-by-step analysis on different time scales such as hours, days, and weeks. Within each time step, the water quality changes in each region are recorded, such as the fluctuation trend of the pH value, the change range of the dissolved oxygen concentration, and the fluctuation range of the turbidity. By performing multi-dimensional space-time coupling on the water quality data, a water quality distribution field for the water conservancy system region is constructed. This distribution field can provide detailed water quality conditions at different time points and in different regions, and finally obtain the corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration, and water conservancy microorganism concentration in the water conservancy system region at each time step.

[0069] Step S3: Based on the water conservancy pH value, water conservancy dissolved oxygen concentration, and water conservancy heavy metal concentration corresponding to the water conservancy system region at each time step, evaluate the microbial growth efficiency of the water conservancy microorganism concentration corresponding to the water conservancy system region at each time step to obtain the microbial growth influence efficiency in the water conservancy region; obtain the water body pollution distribution area corresponding to the water conservancy system region at each time step, and based on the water body pollution distribution area corresponding to the water conservancy system region at each time step and the water conservancy turbidity, perform microbial pollution attenuation analysis on the corresponding water conservancy microorganism concentration to obtain the microbial pollution degradation attenuation rate in the water conservancy region;

[0070] In the embodiments of the present invention, based on the analysis of water quality data, by using known water quality biological reaction models (such as the Michaelis-Menten kinetic model), combined with the pH value, dissolved oxygen concentration and heavy metal concentration in the water conservancy area, the growth influence efficiency of microorganisms is evaluated. These water quality indicators have an important impact on the growth and reproduction of microorganisms. For example, low dissolved oxygen concentration and high heavy metal concentration will inhibit the growth of microorganisms, while suitable pH value helps the reproduction of microorganisms. According to the changes in water quality indicators in different time steps, by calculating the parameters of the growth influence efficiency model, the growth situation of microorganisms in the water body is obtained, and by using mathematical formulas or simulation models, the growth rate, reproduction quantity and distribution in the water body of microorganisms in each area are calculated, so as to obtain the growth influence efficiency of microorganisms in the water conservancy area. Further, by using water quality monitoring instruments, such as turbidity meters, particle size analyzers or suspended solid monitoring systems, the types, concentrations and distributions of suspended solids in the water body are recorded in detail. The distribution of suspended solids can be obtained by setting multiple sampling points or using remote sensing technology, and combined with the water body flow model to analyze the temporal and spatial changes of suspended solids for pollutant distribution quantification. A common method for pollution distribution quantification is based on the combination of geographic information system (GIS) and hydrodynamic model. By three-dimensional flow field simulation, the diffusion range of water body suspended solids at different time steps is predicted, and the corresponding water body pollution distribution area can be obtained by integration. Then, by establishing a decay model of microbial pollution distribution according to the water body pollution distribution area and water body turbidity in the water conservancy system area at different time steps. To achieve this goal, a water quality monitoring system is used to collect water body pollution distribution data at different time periods, including suspended solid concentration, microbial concentration and water body turbidity in the water body, etc. Through numerical simulation methods, using hydrodynamic models (such as two-dimensional water flow models, basin models, etc.), these data are input into the decay equation to calculate the microbial pollution distribution at the corresponding time step, and further calculate the half-life: wherein, is the half-life, k is the decay constant. At the same time, through the previously obtained decay half-life, the pollution degradation attenuation of the water body pollution distribution is quantified. According to the decay rate of microbial pollution, combined with the input data of different water body pollution sources, a method combining the pollution source model and the decay model is used to quantify the degradation amount of microbial pollution: wherein, is the pollution degradation amount at the decay half-life C 0 is the initial pollution concentration distribution of the water body, is the microbial pollution concentration at the decay half-life And by using the previously quantified pollution degradation amount, a time series analysis method is adopted. According to the time change and pollution degradation amount, the degradation rate of microbial pollution in the water body is calculated: wherein, δv is the degradation attenuation rate of microbial pollution in the water conservancy area, is the pollution degradation amount at the decay half-life Δt is the decay time step. By calculating the degradation amount of each time step in segments, the degradation rate in different time periods can be obtained. This rate value can be used to evaluate the purification effect of water pollution, and finally the degradation attenuation rate of microbial pollution in the water conservancy area can be obtained.

[0071] Step S4: Quantify the water quality pollution anomaly of the water quality index dataset of the water conservancy system based on the influence efficiency of microbial growth in the water conservancy area and the degradation attenuation rate of microbial pollution in the water conservancy area to obtain the degree of water quality pollution anomaly in the water conservancy system area; make a decision management on the corresponding regional water quality in the water conservancy system based on the degree of water quality pollution anomaly in the water conservancy system area, generate a water quality anomaly category management strategy corresponding to the water conservancy system area, and execute the corresponding water quality category management work of the water conservancy system area.

[0072] In the embodiment of the present invention, by using the obtained microbial growth efficiency in the water conservancy area and the degradation attenuation rate of microbial pollution, a quantitative analysis of the water quality pollution anomaly of the water conservancy system is carried out. The microbial pollution attenuation rate usually depends on the turbidity of the water area, the pollutant concentration of the water body and other water quality indicators. For example, in areas with higher turbidity, the microbial pollution attenuation rate is lower because the suspended particles in the water produce a shielding effect on the action of microorganisms, thus affecting their efficiency of degrading pollutants. On this basis, by comparing the water quality changes in different periods or different regions, especially the fluctuations in heavy metal concentration and microbial concentration, the degree of water quality pollution anomaly is quantified. By setting a threshold range, the situation where the water quality index exceeds the normal range is regarded as a water quality pollution anomaly, so as to obtain the degree of water quality pollution anomaly in the water conservancy system area. On the basis of the quantification of water quality pollution anomaly, combined with the specific situation of the water conservancy area, methods such as decision trees and fuzzy control algorithms are used for decision management of water quality anomaly categories. According to the different degrees of anomaly, water quality pollution is divided into different categories such as Class I pollution, Class II pollution, and Class III pollution. Each category corresponds to different treatment strategies. For example, Class I pollution only needs to strengthen monitoring, while Class III pollution requires emergency water treatment measures such as adding medicine for disinfection and diverting water for dilution. In addition, combined with the ecological carrying capacity of the water area, based on the quantification results of water quality pollution anomaly, more refined water quality management measures can be formulated, targeted emergency plans and long-term management strategies can be formulated to ensure that the water quality of the water conservancy system is within a safe range, and finally a water quality anomaly category management strategy corresponding to the water conservancy system area is generated to execute the corresponding water quality category management work of the water conservancy system area.

[0073] Further, as an embodiment of the present invention, referring to Figure 2 shown, for Figure 1Schematic diagram of the detailed step flow of step S1. In this embodiment, step S1 includes the following steps:

[0074] Step S11: Obtain the regional scale and water area type distribution corresponding to the water conservancy system;

[0075] In the embodiment of the present invention, the geographical information of the water conservancy system is obtained through the Geographic Information System (GIS) and remote sensing technology, including the specific distribution of various water bodies, such as rivers, lakes, artificial reservoirs, irrigation channels, etc. According to these data, first determine the regional scope where the water conservancy system is located, obtain the area, length-width ratio, and terrain characteristics of this region, and then, combined with the type of water area, divide different categories of water areas. For example, water areas can be divided into rivers, lakes, artificial reservoirs, channels, etc. Further analyze the spatial distribution, morphological characteristics, and area ratio of each water area type within the region. Through these steps, a detailed map of the regional scale and water area type of the water conservancy system is generated, and finally, the regional scale and water area type distribution corresponding to the water conservancy system are obtained.

[0076] Step S12: Conduct a regional hydrodynamic sensitivity gradient analysis on the water conservancy system based on the regional scale and water area type distribution corresponding to the water conservancy system, so as to use Computational Fluid Dynamics (CFD) to simulate the flow velocity, pressure, and turbulence intensity of the water conservancy system under different terrain scales and water area distributions, analyze the corresponding regional hydrodynamic sensitivity gradient distribution, and generate a hydrodynamic sensitivity gradient distribution field for the water conservancy region;

[0077] In the embodiment of the present invention, the water conservancy system is modeled by using Computational Fluid Dynamics (CFD) software, inputting the terrain data, meteorological data, and water area type distribution of the water conservancy region, and setting corresponding boundary conditions according to different water area types, such as water flow velocity, flow rate, and water depth of the water area. Then, use the CFD simulation tool to conduct water flow simulation, calculate the hydrodynamic parameters such as flow velocity, pressure, and turbulence intensity under different terrain and water area distribution conditions. These simulation results can show the movement law of the water flow, and further analyze the hydrodynamic sensitivity gradient under different regions and different water area types. These sensitivity gradients reflect the degree of change of the water flow within the region, providing a guiding basis for subsequent sensor layout. Especially in regions with large hydrodynamic changes, through the comprehensive analysis of flow velocity, pressure, and turbulence intensity, a hydrodynamic sensitivity gradient distribution field for the water conservancy region is generated to show the hydrodynamic sensitivity degree of each region, and finally, a hydrodynamic sensitivity gradient distribution field for the water conservancy region is generated.

[0078] Step S13: Conduct a sensing planning layout for the hydrodynamic sensitivity gradient distribution field of the water conservancy region, so as to set a comprehensive sensing and monitoring node every 5 - 10 kilometers at the corresponding medium-high gradient distribution regions within the hydrodynamic sensitivity gradient distribution field of the water conservancy region, so as to generate a sensing planning layout node for the water conservancy system;

[0079] In the embodiments of the present invention, by identifying the medium-high gradient regions in the previously generated hydrodynamic sensitive gradient distribution field, these regions usually correspond to regions with relatively intense hydrodynamic changes, such as regions with large flow velocity changes or complex water flow phenomena such as vortices. In these regions, an integrated sensing and monitoring node is deployed every 5 to 10 kilometers to ensure that the water flow, meteorology, and water quality changes in these regions can be monitored in real time. Specifically, the hydrodynamic sensitive gradient distribution field can be overlaid with the geographical information of the water conservancy area through GIS technology to determine the positions of the sensing nodes. The layout of these nodes should not only consider the water flow dynamics but also the coverage range and deployment cost of the sensors to ensure that the monitoring network has a high spatial resolution and comprehensiveness, and finally generate the sensing planning and deployment nodes of the water conservancy system.

[0080] Step S14: Deploy corresponding pH sensors, dissolved oxygen sensors, turbidity sensors, heavy metal sensors, and microbial monitoring sensors inside each sensing planning and deployment node of the water conservancy system and connect them to generate a distributed IoT sensing network;

[0081] In the embodiments of the present invention, at each sensing node previously planned and generated, multiple water quality sensors are sequentially deployed. Each sensor is selected and installed according to the water quality monitoring requirements. Specifically, a pH sensor is used to monitor the acidity and alkalinity of the water body in real time, a dissolved oxygen sensor is used to measure the dissolved oxygen concentration in the water, a turbidity sensor is used to reflect the turbidity of the water body, a heavy metal sensor is used to monitor the heavy metal concentration in the water (such as lead, mercury, arsenic, etc.), and a microbial monitoring sensor is used to detect the microbial community in the water body. The installation position and depth of the sensors need to be optimized according to the characteristics of the water area to ensure that all water quality parameters can be comprehensively covered. Each sensor is connected into a distributed IoT sensing network through wireless communication technologies (such as LoRa, Zigbee, or cellular networks, etc.) to realize the remote transmission of real-time data. The design of the sensing network should ensure stable and low-latency data transmission between sensors and be able to support the efficient processing of large-scale data, and finally connect to generate a distributed IoT sensing network.

[0082] Step S15: Use the distributed IoT sensing network to monitor the water quality indicators of the water conservancy system in real time to obtain a water quality indicator dataset of the water conservancy system, which includes the pH value of the water conservancy area, the dissolved oxygen concentration of the water conservancy area, the turbidity of the water conservancy area, the heavy metal concentration of the water conservancy area, and the microbial concentration of the water conservancy area.

[0083] In the embodiments of the present invention, water quality data of each sensing node is collected in real time through a deployed distributed IoT sensing network. These data include water quality indicators such as pH value, dissolved oxygen concentration, turbidity, heavy metal concentration, and microbial concentration of each water area. During the data collection process, the sensor makes a measurement every certain period of time (such as every minute or every hour), and transmits the results to the centralized processing system through a wireless network. The data collection system needs to have functions of data cleaning, preprocessing, and outlier detection to ensure the accuracy and reliability of the monitoring data. For outliers, a real-time alarm mechanism can be used for reminder so as to take corresponding measures in time. By monitoring these water quality data in real time, the water quality status of the water conservancy system can be comprehensively understood. In addition, the collected water quality data will be stored in a database to form a detailed water quality indicator dataset, and finally a water conservancy system water quality indicator dataset is obtained, including the pH value of the water conservancy area, the dissolved oxygen concentration of the water conservancy area, the turbidity of the water conservancy area, the heavy metal concentration of the water conservancy area, and the microbial concentration of the water conservancy area.

[0084] Further, as an embodiment of the present invention, referring to Figure 3 shown, it is Figure 1 a detailed step flow schematic diagram of step S2 in

[0085] Step S21: Obtain the water conservancy geographical spatial location distribution and water conservancy topographic and geomorphic elevation distribution corresponding to the water conservancy system;

[0086] In the embodiments of the present invention, the geographical spatial information of the area where the water conservancy system is located is obtained from satellite images, aerial photos, or publicly available geographical spatial data resources through remote sensing technology or geographic information system (GIS). Specifically in implementation, GIS software (such as ArcGIS or QGIS) can be used to extract the geographical coordinates, boundaries, and corresponding elevation data of the areas where water conservancy facilities (such as reservoirs, pumping stations, rivers, lakes, etc.) are located. The elevation data can usually be obtained through a digital elevation model (DEM). The DEM can accurately represent the undulating changes of the terrain. By combining these spatial information, a geographical location distribution map and an elevation distribution map of the water conservancy system are constructed. This dataset can be further refined, especially in cases where water flow paths or hydrological characteristics need to be considered, for watershed division and hydrological model analysis, and finally the water conservancy geographical spatial location distribution and water conservancy topographic and geomorphic elevation distribution are obtained.

[0087] Step S22: Perform a water conservancy spatial distribution simulation on the water conservancy system based on the water conservancy geographical spatial location distribution and water conservancy topographic and geomorphic elevation distribution to generate a water conservancy system regional spatial simulation distribution field;

[0088] In the embodiment of the present invention, based on the water conservancy geospatial location and terrain elevation data obtained in the previous step, a spatial distribution simulation of the water conservancy system is carried out. This simulation can adopt a watershed hydrological model (such as the SWAT model, the HEC-HMS model, etc.) to simulate the flow path and hydraulic characteristics of water flow in different topographies and landforms. During the simulation process, the input water conservancy geographic information and elevation data will be used as initial conditions. The model will predict the changes in the distribution, flow velocity, flow rate, etc. of the water flow according to factors such as terrain undulation, river channel slope, soil type, etc. Through this process, a spatial simulation distribution field of the water conservancy system area can be generated, including information such as the distribution range, flow direction, and water depth of the water body, and finally a spatial simulation distribution field of the water conservancy system area is generated.

[0089] Step S23: Perform spatio-temporal scale fluctuation analysis on each water quality index in the water conservancy system water quality index dataset to obtain the spatio-temporal scale change fluctuation distribution corresponding to each water conservancy system water quality index;

[0090] In the embodiment of the present invention, by collecting water quality monitoring data in the water conservancy system area, these data usually include indicators such as the pH value of the water body, dissolved oxygen concentration, turbidity, heavy metal concentration, and microorganism concentration. By analyzing the data at different time points and different spatial regions, spatio-temporal analysis methods (such as spatio-temporal series analysis or spatio-temporal regression analysis) are applied to model the fluctuation characteristics of the water quality index. Specifically, a spatio-temporal model (such as the spatio-temporal autoregressive model SAR) can be used to analyze the change laws of different water quality indexes at different time and space scales, identify the change fluctuations of the water quality index with geographical location in space, and the fluctuation characteristics with seasons or climate change in time. This analysis helps to reveal the seasonal fluctuations, spatial differences, and long-term and short-term trend changes of the water quality, and finally obtain the spatio-temporal scale change fluctuation distribution corresponding to each water conservancy system water quality index.

[0091] Step S24: Couple the regional water quality index distribution to the spatial simulation distribution field of the water conservancy system area based on the spatio-temporal scale change fluctuation distribution corresponding to each water conservancy system water quality index to generate the water quality index distribution field of the water conservancy system area;

[0092] In an embodiment of the present invention, by using the previously obtained spatio-temporal scale fluctuation analysis results, a coupled analysis of the regional water quality of the water conservancy system is carried out. Specifically, during implementation, first, the spatio-temporal fluctuation distribution of water quality indicators is combined with the regional space simulation distribution field of the water conservancy system. Through a hydrological and water quality coupling model (such as the hydrological and water quality model MIKE21, CE-QUAL-W2, etc.), the water quality fluctuation model is embedded into the water flow simulation model to generate a water quality indicator field containing spatial distribution characteristics. This process can, based on the spatio-temporal fluctuation analysis results, calculate the distribution of different water quality indicators at each spatial position and consider the influence of factors such as water flow, terrain, and climate. The coupled water quality distribution field can reflect the regional water quality status of the water body, including characteristics such as the spatial distribution of pollution sources, the concentration gradient of pollutants, and the water body self-purification process, and finally generate a regional water quality indicator distribution field of the water conservancy system.

[0093] Step S25: Perform regional time step division on the regional water quality indicator distribution field of the water conservancy system to obtain the corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration, and water conservancy microorganism concentration in the water conservancy system region at each time step.

[0094] In an embodiment of the present invention, by performing time step division on the water conservancy system region based on the previously generated water quality indicator distribution field. Specifically, the water quality indicator distribution data is segmented according to the actual monitoring period (such as daily, monthly, seasonal, or annual) for precise analysis of water quality changes in different time periods. Within each time step, the changes in various water quality indicators during that period are calculated and extracted, covering indicators such as pH value, dissolved oxygen concentration, turbidity, heavy metal concentration, and microorganism concentration. To improve the accuracy and reliability of the data, historical data and real-time monitoring data can be combined, and time series analysis methods (such as ARIMA model, Kalman filter, etc.) are used to smooth and predict the data. Through time step division, the water quality indicator values at different positions in the water conservancy system region at each time point are obtained, and finally, the corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration, and water conservancy microorganism concentration in the water conservancy system region at each time step are obtained.

[0095] Further, step S3 includes the following steps:

[0096] Step S31: Obtain the corresponding water conservancy microorganism growth and reproduction amount through the water conservancy microorganism concentration corresponding to each time step in the water conservancy system region;

[0097] In the embodiments of the present invention, the concentration of water conservancy microorganisms in this area is obtained through sensors and sampling devices. This process usually relies on water quality monitoring instruments such as spectrometers, flow cytometers, or molecular biology techniques (such as PCR, qPCR) for real-time monitoring and analysis. These devices can provide the concentration data of microorganisms in water and record it in the form of a time series. Subsequently, according to the change trend of the microorganism concentration, biological models such as the Monod equation or other microorganism growth models can be applied to calculate the growth and reproduction amount of water conservancy system microorganisms in this time step. Specifically, the growth amount can be calculated through the following formula: ΔC m = μ max × C m × Δt, where μ max is the maximum growth rate of microorganisms, C m is the microorganism concentration at the current time step, and Δt is the time step length, and finally the corresponding growth and reproduction amount of water conservancy microorganisms is obtained.

[0098] Step S32: Calculate the growth rate of the water conservancy microorganisms corresponding to the water conservancy system area at each time step to obtain the growth rate of the water conservancy microorganisms corresponding to the water conservancy system area at each time step;

[0099] In the embodiments of the present invention, once the growth and reproduction amount of water conservancy microorganisms at each time step is obtained, next, the growth rate of microorganisms in the water conservancy system area at each time step needs to be calculated. This process is usually carried out by the method of continuous data analysis. First, according to the growth amount of microorganisms at each time step, the growth rate of microorganisms between each time step is calculated by using the difference method or the fitting method. Specifically, the following growth rate calculation formula can be adopted: where μ(t) is the growth rate of water conservancy microorganisms corresponding to the water conservancy system area at time step t, and finally the growth rate of water conservancy microorganisms corresponding to the water conservancy system area at each time step is obtained.

[0100] Step S33: Based on the water conservancy pH value, water conservancy dissolved oxygen concentration, and water conservancy heavy metal concentration corresponding to the water conservancy system area at each time step, evaluate the microorganism growth efficiency of the water conservancy microorganisms corresponding to the water conservancy system area at each time step to obtain the microorganism growth influence efficiency of the water conservancy area;

[0101] In an embodiment of the present invention, by combining environmental data such as pH value, dissolved oxygen concentration, and heavy metal concentration measured in the water conservancy system area at each time step with the microbial growth rate, the evaluation of microbial growth efficiency is carried out. When specifically implemented, a growth efficiency evaluation model, such as a modified Monod model, can be established to quantify the influence of each environmental factor on the microbial growth rate, so as to obtain the influence efficiency of the microbial growth rate in the water conservancy system area, and finally obtain the influence efficiency of the water conservancy area on microbial growth.

[0102] Step S34: Obtain the distribution of water body suspended solids corresponding to the water conservancy system area at each time step, and quantify the pollution distribution according to the distribution of water body suspended solids corresponding to the water conservancy system area at each time step to obtain the water body pollution distribution area corresponding to the water conservancy system area at each time step;

[0103] In an embodiment of the present invention, the process of obtaining the distribution of suspended solids in the water body needs to use water quality monitoring instruments, such as turbidimeters, particle size analyzers, or suspended solid monitoring systems, to record in detail the types, concentrations, and distributions of suspended solids in the water body. The distribution of suspended solids can be obtained by setting multiple sampling points or using remote sensing technology, and the spatio-temporal changes of suspended solids are analyzed in combination with the water body flow model. After data collection, by combining the suspended solid concentrations at different positions with parameters such as water body flow velocity and flow direction, the distribution of pollutants is quantified. A common method for pollution distribution quantification is based on the combination of geographic information system (GIS) and hydrodynamic models, and the diffusion range of water body suspended solids at different time steps is predicted through three-dimensional flow field simulation. Specifically, the pollution distribution can be quantified according to the following formula: pollution area = ∫∫ Area S(x, y, t)dxdy, where S(x, y, t) represents the suspended solid concentration at time t and position (x, y). By integrating, the total area of water body pollution can be obtained, and finally the water body pollution distribution area corresponding to the water conservancy system area at each time step is obtained.

[0104] Step S35: Based on the water body pollution distribution area corresponding to the water conservancy system area at each time step and the water conservancy turbidity, perform microbial pollution attenuation analysis on the corresponding water conservancy microbial concentration to obtain the microbial pollution degradation attenuation rate in the water conservancy area.

[0105] In an embodiment of the present invention, an attenuation model of microbial pollution distribution is established based on the water body pollution distribution area and water body turbidity of the water conservancy system area at different time steps. To achieve this goal, a water quality monitoring system is used to collect water body pollution distribution data at different time periods, including suspended solid concentration, microbial concentration, and water body turbidity in the water body. Through numerical simulation methods, these data are input into the attenuation equation using hydrodynamic models (such as two-dimensional water flow models, basin models, etc.), and the microbial pollution distribution at the corresponding time step is calculated. During the simulation process, the physical and chemical characteristics of the water body and the characteristics of the microbial community need to be combined to accurately estimate the microbial pollution distribution and attenuation process at different time points, and determine the microbial pollution attenuation half-life of the water conservancy system area at different time steps. Then, a standard attenuation model (such as a first-order attenuation model, a second-order attenuation model, etc.) is used for analysis. According to the law of microbial concentration changing with time, the attenuation constant is solved by fitting the data, and the half-life is further calculated: Wherein, is the half-life, k is the attenuation constant. At the same time, based on the previously obtained attenuation half-life, the pollution degradation attenuation of the water body pollution distribution is quantified. According to the attenuation rate of microbial pollution, combined with the input data of different water pollution sources, a method combining a pollution source model and an attenuation model is used to quantify the degradation amount of microbial pollution, and the degradation amount can be expressed by the following formula: Wherein, is the pollution degradation amount at the attenuation half-life , C 0 is the initial pollution concentration distribution of the water body, is the microbial pollution concentration at the attenuation half-life . Then, by using the previously quantified pollution degradation amount data and adopting time series analysis methods, according to the time change and pollution degradation amount, the degradation rate of microbial pollution in the water body is calculated. When calculating the degradation rate, the standard calculation formula of the microbial attenuation rate can be used: Wherein, δ v is the degradation attenuation rate of microbial pollution in the water conservancy area, is the pollution degradation amount at the attenuation half-life , and Δt is the attenuation time step. By calculating the degradation amount of each time step in segments, the degradation rate in different time periods can be obtained, and this rate value can be used to evaluate the water body pollution purification effect, and finally the degradation attenuation rate of microbial pollution in the water conservancy area is obtained.

[0106] Further, step S33 includes the following steps:

[0107] Based on the water conservancy pH value and water conservancy dissolved oxygen concentration corresponding to the water conservancy system area at each time step, the heavy metal redox impact analysis is carried out on the corresponding water conservancy heavy metal concentration, and the heavy metal redox state impact coefficient corresponding to the water conservancy system area at each time step is obtained;

[0108] In the embodiment of the present invention, through the pH value and dissolved oxygen concentration of the water body at each time step, combined with the heavy metal elements existing in the water body, the impact analysis of the redox reaction is carried out. The redox state of heavy metals has an important impact on their solubility, mobility and toxicity in the water body. To achieve this analysis, first, the water body pH value and dissolved oxygen concentration data at each time step are obtained through water quality detection instruments (such as pH meters, dissolved oxygen detectors, ICP-MS, etc.). Then, using the standard electrode potential of heavy metals and the corresponding redox reaction equations, the changes in the redox states of various heavy metals in the water body under different pH values and dissolved oxygen conditions are calculated through a compiled mathematical model (such as the Nernst equation). For example, under acidic conditions, some heavy metals tend to be in a high valence state, and under oxygen-rich conditions, a reduction reaction will occur and they will be converted into a low valence state. Through these calculations, the heavy metal redox state impact coefficients under different water body conditions at each time step are obtained, and finally the heavy metal redox state impact coefficients corresponding to the water conservancy system area at each time step are obtained.

[0109] Preferably, according to the heavy metal redox state impact coefficients corresponding to the water conservancy system area at each time step, the growth substrate heavy metal affinity analysis is carried out on the corresponding microbial growth process in the water conservancy system area, and the microbial growth substrate heavy metal affinity constant corresponding to the water conservancy system area at each time step is obtained;

[0110] In the embodiment of the present invention, through the use of the previously obtained heavy metal redox state impact coefficients, combined with the growth requirements of microorganisms and their affinity for heavy metals, the growth substrate heavy metal affinity analysis of microorganisms is carried out. In the specific implementation process, the experimental data of environmental microorganism culture are used to obtain the affinity constants of microorganisms for different heavy metals. These constants are determined according to the affinity of heavy metal ions for the cell membrane surface of microorganisms. By monitoring the redox state of heavy metals in the water body, combined with literature data and laboratory research results, the influence rules of heavy metal ions on the growth substrate of microorganisms under different pH values and dissolved oxygen concentrations are formulated. Based on these data, an affinity model of microorganisms for heavy metals in the water body is constructed. Through this model, the heavy metal affinity constants of microorganisms at each time step can be calculated, reflecting the influence of heavy metals in the water body on the microbial growth process, and finally the microbial growth substrate heavy metal affinity constants corresponding to the water conservancy system area at each time step are obtained.

[0111] Preferably, based on the water conservancy pH value, water conservancy dissolved oxygen concentration, and heavy metal affinity constant of the microbial growth substrate corresponding to the water conservancy system area at each time step, the microbial growth efficiency evaluation of the water conservancy microbial growth rate corresponding to the water conservancy system area at each time step is carried out by using the microbial growth influence calculation formula, and the microbial growth influence efficiency of the water conservancy area is obtained.

[0112] In the embodiment of the present invention, by combining the total number corresponding to the time step, the time step length, the time step variable parameter, the water conservancy microbial growth rate, the water conservancy microbial substrate concentration, the water conservancy microbial substrate half-saturation constant, the water conservancy dissolved oxygen concentration, the water conservancy dissolved oxygen half-saturation constant, the water conservancy pH value, the microbial growth inhibition coefficient, the heavy metal concentration, the heavy metal half-saturation constant, the heavy metal affinity constant of the microbial growth substrate, and related parameters, a suitable microbial growth influence calculation formula is constructed to evaluate the microbial growth efficiency of the water conservancy microbial growth rate corresponding to the water conservancy system area at each time step, so as to determine the optimal growth conditions of microorganisms according to the pH value and dissolved oxygen concentration of the water body, and then calculate the growth influence efficiency of microorganisms under these conditions, and finally obtain the microbial growth influence efficiency of the water conservancy area.

[0113] Further, the microbial growth influence calculation formula is specifically:

[0114]

[0115] In the formula, μ s is the microbial growth influence efficiency of the water conservancy area, n is the total number corresponding to the time step, i is the item index corresponding to the time step, T i is the i-th time step length, t is the time step variable parameter, μ(t) is the water conservancy microbial growth rate corresponding to the water conservancy system area at time step t, S(t) is the water conservancy microbial substrate concentration corresponding to the water conservancy system area at time step t, K S is the water conservancy microbial substrate half-saturation constant, O(t) is the water conservancy dissolved oxygen concentration corresponding to the water conservancy system area at time step t, K O is the water conservancy dissolved oxygen half-saturation constant, exp is the exponential function, pH(t) is the water conservancy pH value corresponding to the water conservancy system area at time step t, α is the microbial growth inhibition coefficient, M(t) is the heavy metal concentration corresponding to the water conservancy system area at time step t, K M is the heavy metal half-saturation constant, ε is the heavy metal affinity constant of the microbial growth substrate, and η is the correction coefficient of the microbial growth influence efficiency of the water conservancy area.

[0116] The present invention obtains a calculation formula for the influence of microbial growth through the use of a specific mathematical model and verification, which is used to evaluate the microbial growth efficiency of the growth rate of water conservancy microorganisms corresponding to each time step in the water conservancy system area. This calculation formula for the influence of microbial growth is used to evaluate the growth efficiency of microorganisms in the water conservancy system area, taking into account multiple important factors, including substrate concentration, dissolved oxygen concentration, pH value, heavy metal concentration, etc. The influence of these factors on microbial growth has profound significance in ecology and environmental biology. Among them, the substrate concentration represents the concentration of substrates (such as organic matter or nutrients) available for microorganisms in the water conservancy area, and the half-saturation constant is the affinity of microorganisms for substrates during growth. The smaller the half-saturation constant, the stronger the affinity of microorganisms for substrates, and the maximum growth rate can be achieved at a lower substrate concentration. This term describes how microorganisms are restricted by substrate concentration. When the substrate concentration is low, the microbial growth rate is restricted; when the substrate concentration increases, the microbial growth rate approaches the maximum value. The dissolved oxygen concentration is the content of dissolved oxygen in water, which directly affects the respiration and growth of aerobic microorganisms. The corresponding half-saturation constant describes the affinity of microorganisms for oxygen. The smaller the value, the more sensitive the microorganisms are to the demand for oxygen. This term indicates the supporting effect of dissolved oxygen concentration on microbial growth. The higher the oxygen concentration, the more effectively microorganisms can carry out metabolism and growth. When the dissolved oxygen concentration is low, the microbial growth rate is restricted. Secondly, the influence of pH value on microbial growth Among them, the pH value affects the metabolic activities and cell functions of microorganisms. Too high or too low pH value will inhibit the growth of microorganisms; together with the heavy metal half-saturation constant, the heavy metal concentration describes the inhibitory effect of heavy metals on microbial growth. The heavy metal affinity constant of the microbial growth substrate characterizes the sensitivity of microorganisms to heavy metals. If the value is high, it means that microorganisms are greatly inhibited in the presence of heavy metals. This term reveals the inhibitory effect of the combined action of pH value and heavy metal concentration on microorganisms. The change of pH value will exacerbate or slow down the influence of heavy metals, resulting in the change of microbial growth rate. When the pH value deviates from the appropriate range, even if the heavy metal concentration is low, a significant inhibitory effect will be produced. By accumulating the influence of microbial growth over the entire time period through time step accumulation, the changes of influencing factors at different time steps can be considered to comprehensively evaluate the change of microbial growth efficiency during the entire research period, so as to reflect the comprehensive influence of fluctuations at different time steps in the ecological environment on microbial growth. In addition, by introducing a correction coefficient to adjust the non-linear factors in the model or other influencing factors not fully included in the formula, it allows the calculation formula to be flexibly adjusted in specific situations, making the calculation results closer to the microbial growth process in the real ecosystem. To sum up, this formula fully considers the influence efficiency μ of microorganisms in the water conservancy area s, the total number n corresponding to the time step, the item index i corresponding to the time step, the i-th time step length T i , the time step variable parameter t, the growth rate μ(t) of water conservancy microorganisms corresponding to the water conservancy system area at time step t, the substrate concentration S(t) of water conservancy microorganisms corresponding to the water conservancy system area at time step t, the half-saturation constant K of the water conservancy microorganism substrate S , the dissolved oxygen concentration O(t) of water conservancy corresponding to the water conservancy system area at time step t, the half-saturation constant K of the water conservancy dissolved oxygen O , the exponential function exp, the pH value pH(t) of water conservancy corresponding to the water conservancy system area at time step t, the microbial growth inhibition coefficient α, the heavy metal concentration M(t) corresponding to the water conservancy system area at time step t, the half-saturation constant K of the heavy metal M , the heavy metal affinity constant ε of the microbial growth substrate, the correction coefficient η of the growth influence efficiency of water conservancy area microorganisms, according to the growth influence efficiency μ of water conservancy area microorganisms s The mutual correlation relationship with the above parameters constitutes a functional relationship:

[0117]

[0118] This formula can realize the evaluation process of the microbial growth efficiency of the growth rate of water conservancy microorganisms corresponding to the water conservancy system area at each time step. At the same time, by introducing the correction coefficient η of the growth influence efficiency of water conservancy area microorganisms, it can be adjusted according to the error situation in the calculation process, thereby improving the accuracy and applicability of the calculation formula of the microbial growth influence.

[0119] Further, step S35 includes the following steps:

[0120] Step S351: Based on the water pollution distribution area corresponding to the water conservancy system area at each time step and the water conservancy turbidity, perform a simulation of the attenuation of the corresponding water conservancy microorganism concentration for microbial pollution distribution to generate the attenuation process of the microbial pollution distribution corresponding to the water conservancy system area at each time step;

[0121] In the embodiment of the present invention, by establishing an attenuation model of microbial pollution distribution based on the water body pollution distribution area and water body turbidity of the water conservancy system area at different time steps, to achieve this goal, a water quality monitoring system is used to collect water body pollution distribution data at different time periods, including suspended solid concentration, microbial concentration, and water body turbidity in the water body, etc. Through numerical simulation methods, using hydrodynamic models (such as two-dimensional water flow models, watershed models, etc.), these data are input into the attenuation equation to calculate the microbial pollution distribution at the corresponding time step. During the simulation process, it is necessary to combine the physical and chemical characteristics of the water body and the characteristics of the microbial community, considering the influence of factors such as water flow movement, sedimentation, diffusion, light, and temperature on the microbial concentration, so as to accurately estimate the microbial pollution distribution and attenuation process at different time points. For example, assuming that the pollution area of the water body in a certain water conservancy system area at a specific time step is 500 square kilometers and the water body turbidity is 1.5 NTU, at this time, by introducing the concentration attenuation model of the water body and combining factors such as rainfall, flow change, and temperature in the watershed, the distribution and attenuation process of microbial pollution in this area at subsequent time steps can be simulated, and finally, the microbial pollution distribution attenuation process corresponding to each time step in the water conservancy system area is simulated and generated.

[0122] Step S352: Determine the attenuation half-life of the microbial pollution distribution attenuation process corresponding to each time step in the water conservancy system area to obtain the microbial pollution attenuation half-life corresponding to each time step in the water conservancy system area;

[0123] In the embodiment of the present invention, by determining the microbial pollution attenuation half-life of the water conservancy system area at different time steps, first, using the previously obtained microbial pollution distribution attenuation process data, standard attenuation models (such as first-order attenuation models, second-order attenuation models, etc.) are used for analysis. According to the law of microbial concentration changing with time, the attenuation constant is solved by fitting the data, and the half-life is further calculated. Specifically, the half-life can be calculated by the curve fitting method using the concentration change during the attenuation process with the following formula: where, is the half-life, and k is the attenuation constant. Through this method, the corresponding microbial pollution attenuation half-life at each time step can be obtained, and the attenuation characteristics of microbial pollution in the water body at different time points can be reflected. For example, if the attenuation process of microbial concentration shows an exponential decline trend at a certain time step, the half-life of microbial pollution at this moment can be calculated to be 12 hours using the known attenuation constant. This value reflects the self-purification ability of microbial pollution in the water body and the influence of the external environment, and finally, the microbial pollution attenuation half-life corresponding to each time step in the water conservancy system area is obtained.

[0124] Step S353: Quantify the pollution degradation attenuation of the water pollution distribution in the corresponding microbial pollution distribution attenuation process based on the microbial pollution attenuation half-life corresponding to the water conservancy system area at each time step, to obtain the microbial water pollution degradation attenuation amount corresponding to the water conservancy system area at each time step;

[0125] In the embodiment of the present invention, through the previously obtained attenuation half-life data, the pollution degradation attenuation of the water pollution distribution is quantified. According to the attenuation rate of the microbial pollution, combined with the input data of different water pollution sources, a method combining a pollution source model and an attenuation model is adopted to quantify the degradation amount of the microbial pollution. The specific operation includes weighting the contributions of different pollution sources to the water body microbial pollution concentration, and combining the microbial pollution attenuation process at each time step to calculate the corresponding pollution degradation amount. This degradation amount can be expressed by the following formula: Among them, is the pollution degradation amount at the attenuation half-life , C 0 is the initial water pollution concentration distribution, is the microbial pollution concentration at the attenuation half-life This step can perform numerical calculations through numerical simulation software (such as MATLAB, ArcGIS, etc.) combined with the physical and chemical parameters of the water body to obtain the degradation amount of the water pollution at different time steps. For example, in a simulation, the initial microbial concentration of a certain water area is 100 CFU / mL. By calculating the attenuation model, the microbial concentration after 6 hours is 50 CFU / mL, and the pollution degradation amount is 50 CFU / mL. The entire pollution attenuation process can be accurately quantified, and finally the microbial water pollution degradation attenuation amount corresponding to the water conservancy system area at each time step is obtained.

[0126] Step S354: Calculate the attenuation rate based on the microbial water pollution degradation attenuation amount corresponding to the water conservancy system area at each time step, to obtain the microbial pollution degradation attenuation rate of the water conservancy area.

[0127] In the embodiment of the present invention, by using the previously quantified pollution degradation amount data and adopting a time series analysis method, according to the time change and the pollution degradation amount, the degradation rate of the microbial pollution in the water body is calculated. When calculating the degradation rate, the standard calculation formula for the microbial attenuation rate can be used: Among them, δ v is the microbial pollution degradation attenuation rate of the water conservancy area, is the attenuation half-life The amount of pollution degradation, Δt is the decay time step. By calculating the degradation amount in segments for each time step, the degradation rate in different time periods can be obtained. For example, in a certain monitoring, if the microbial pollution degradation amount in the water body is 200 CFU / mL within 24 hours, and the degradation rate is 8.33 CFU / mL / h, this rate value can be used to evaluate the water pollution purification effect, and finally the microbial pollution degradation attenuation rate in the water conservancy area is obtained.

[0128] Further, step S4 includes the following steps:

[0129] Step S41: Based on the influence efficiency of microbial growth in the water conservancy area and the microbial pollution degradation attenuation rate in the water conservancy area, use the regional water quality pollution anomaly calculation formula to quantify the water quality pollution of the water conservancy system water quality index dataset, so as to obtain the degree of water quality pollution anomaly in the water conservancy system area;

[0130] In the embodiment of the present invention, by combining the size of the water conservancy system area, the abscissa of the water conservancy system area location point, the ordinate of the water conservancy system area location point, the water conservancy pH value, the water conservancy dissolved oxygen concentration, the water conservancy turbidity, the water conservancy heavy metal concentration, the water conservancy microbial concentration, the influence efficiency of microbial growth in the water conservancy area, the microbial pollution degradation attenuation rate in the water conservancy area and related parameters, a suitable regional water quality pollution anomaly calculation formula is formed to quantify the water quality pollution of the water conservancy system water quality index dataset, so as to calculate the anomaly degree of water quality pollution at each monitoring point, and finally the degree of water quality pollution anomaly in the water conservancy system area is obtained.

[0131] Step S42: Based on the degree of water quality pollution anomaly in the water conservancy system area, determine and divide the corresponding regional water quality in the water conservancy system into anomaly categories, and generate the water quality anomaly categories corresponding to the water conservancy system area, including water quality anomaly type I, water quality anomaly type II and water quality anomaly type III;

[0132] In the embodiments of the present invention, based on the degree of water quality pollution anomaly obtained previously, the anomaly category determination of the water quality in the water conservancy system area is carried out. In the specific implementation operation, first, data analysis is performed on the quantified data, and the degree of water quality pollution anomaly at each water quality monitoring point is compared with a preset classification standard. The standard is formulated based on the historical water quality data, geographical features of the area, and the influence degree of various water pollutants on the water quality. According to the magnitude of the degree of water quality pollution anomaly, the degree of water quality pollution is divided into three categories: water quality anomaly category I, II, and III. Among them, water quality anomaly category I indicates a relatively light pollution degree, and the water quality can still maintain normal water body functions; water quality anomaly category II indicates a medium pollution degree, and certain treatment measures are required to restore the water quality; water quality anomaly category III indicates a serious water quality pollution, and high-intensity treatment measures are urgently needed. The specific classification method can be by determining a pollution degree threshold. For example, the pollution value between 0 and 0.2 is category I, between 0.2 and 0.5 is category II, and exceeding 0.5 is category III. The water quality monitoring data of each area is judged one by one according to this classification standard to generate the water quality anomaly category corresponding to the area, and finally the water quality anomaly category corresponding to the water conservancy system area is generated.

[0133] Step S43: According to the water quality anomaly category corresponding to the water conservancy system area, conduct anomaly category decision-making management on the corresponding regional water quality in the water conservancy system, and generate a water quality anomaly category management strategy corresponding to the water conservancy system area to perform the corresponding water quality category management work for the water conservancy system area.

[0134] In the embodiments of the present invention, by according to the previously obtained water quality anomaly category, conduct anomaly category decision-making management on the water quality of each area in the water conservancy system and generate the corresponding water quality anomaly category management strategy. In the specific implementation, first, according to the judgment result of the regional water quality anomaly category, specific management strategies are formulated for water quality pollution areas of different categories. For water quality anomaly category I areas, the management strategy includes regularly monitoring the water quality, strengthening the restoration work of the natural ecosystem, or maintaining the water quality through natural restoration measures of plants and microorganisms; for water quality anomaly category II areas, it is necessary to strengthen the control of pollution sources, such as adjusting the pollution discharge of agricultural irrigation, reducing the discharge of industrial wastewater, and increasing the investment in water quality purification facilities, such as using constructed wetlands or building water treatment stations for preliminary purification; for water quality anomaly category III areas, the management strategy should be more stringent, including immediately starting a water quality restoration project, using efficient water treatment technologies (such as membrane filtration, chemical precipitation, ozone treatment, etc.) for thorough purification, or considering measures such as local water body enclosure or water source replacement. The specific management strategy will be automatically generated by the data platform and adjusted regularly according to the pollution degree, historical trend, and external environmental changes of the area to ensure that the water conservancy system can continuously maintain good water quality conditions, and finally generate a water quality anomaly category management strategy corresponding to the water conservancy system area to perform the corresponding water quality category management work for the water conservancy system area.

[0135] Further, the specific formula for the regional water quality pollution anomaly in step S41 is as follows:

[0136]

[0137] In the formula, P is the degree of regional water quality pollution anomaly of the water conservancy system, A is the size of the regional scope of the water conservancy system, x is the abscissa of the location point of the water conservancy system area, y is the ordinate of the location point of the water conservancy system area, pH(x, y) is the water conservancy pH value at the location point (x, y) of the water conservancy system area, O(x, y) is the water conservancy dissolved oxygen concentration at the location point (x, y) of the water conservancy system area, Z(x, y) is the water conservancy turbidity at the location point (x, y) of the water conservancy system area, M(x, y) is the water conservancy heavy metal concentration at the location point (x, y) of the water conservancy system area, C(x, y) is the water conservancy microorganism concentration at the location point (x, y) of the water conservancy system area, μ s is the influence efficiency of microorganism growth in the water conservancy area, δ v is the degradation attenuation rate of microorganism pollution in the water conservancy area, and ξ is the correction coefficient of the regional water quality pollution anomaly degree of the water conservancy system area.

[0138] The present invention obtains a regional water quality pollution anomaly calculation formula through the use of a specific mathematical model and verification, which is used to quantify the water quality pollution anomaly of the water quality index dataset of the water conservancy system. This regional water quality pollution anomaly calculation formula comprehensively considers various key factors affecting water quality, such as pH value, dissolved oxygen concentration, turbidity, heavy metal concentration, microbial concentration, etc. These factors represent different aspects of water quality, including the acidity and alkalinity of water, the sufficiency of dissolved oxygen, pollutants in the water body, and microbial pollution, etc. Through the combination of these factors, the comprehensive pollution state of the water quality in the water conservancy system area can be more accurately reflected. The pH value and dissolved oxygen concentration reflect the acidity and alkalinity of the water body and the redox ability, which are important indicators for judging the health status of water quality. Turbidity reflects the concentration of suspended solids in the water. Turbid water quality contains more harmful substances and microorganisms. Heavy metal concentration reflects that heavy metal pollution in the water body will seriously affect the ecology and water quality health. Microbial concentration reflects the microbial concentration in the water body, which reflects whether the water body is biologically polluted. Secondly, the growth influence efficiency of microorganisms in the water conservancy area is particularly considered in this formula, and the positive effect of the growth of microorganisms in the water body on water quality improvement can be considered. For example, microorganisms can degrade organic matter and certain pollutants, thereby helping to purify the water quality. The attenuation rate of microbial pollution degradation indicates how the degradation ability of microorganisms to pollutants declines over time, so as to dynamically reflect the gradual spread or alleviation of pollution. Through the above formula, the water quality of the water conservancy system area can be quantitatively analyzed according to the water quality factors at different location points, and the pollution anomaly degree of this area can be calculated. The key advantage of this method is that through the location coordinates, the pollution anomaly conditions of different water areas can be accurately described and quantified. Moreover, as the environmental conditions and microbial populations change, the regional pollution anomaly degree will also change, and the formula can update the water quality anomaly state in real time, which is convenient for managers to take corresponding measures in a timely manner. To sum up, this formula fully considers the regional water quality pollution anomaly degree P of the water conservancy system area, the size A of the water conservancy system area, the abscissa x of the location point in the water conservancy system area, the ordinate y of the location point in the water conservancy system area, the water conservancy pH value pH(x,y) at the location point (x,y) in the water conservancy system area, the water conservancy dissolved oxygen concentration O(x,y) at the location point (x,y) in the water conservancy system area, the water conservancy turbidity Z(x,y) at the location point (x,y) in the water conservancy system area, the water conservancy heavy metal concentration M(x,y) at the location point (x,y) in the water conservancy system area, the water conservancy microbial concentration C(x,y) at the location point (x,y) in the water conservancy system area, the growth influence efficiency μ of microorganisms in the water conservancy area s , the attenuation rate δ of microbial pollution degradation in the water conservancy area v , and the correction coefficient ξ of the regional water quality pollution anomaly degree of the water conservancy system area. A functional relationship is formed according to the mutual correlation relationship between the regional water quality pollution anomaly degree P of the water conservancy system area and the above parameters:

[0139]

[0140] This formula can realize the quantification process of water quality pollution anomalies in the water quality index dataset of the water conservancy system. At the same time, by introducing the correction coefficient ξ of the water quality pollution anomaly degree in the water conservancy system area, it can be adjusted according to the error situation in the calculation process, so as to improve the accuracy and applicability of the regional water quality pollution anomaly calculation formula.

[0141] Furthermore, the present invention also provides a water quality monitoring and analysis system for a water conservancy system, which is used to execute the water quality monitoring and analysis method of the water conservancy system as described above. The water quality monitoring and analysis system of the water conservancy system includes:

[0142] The water quality index monitoring module of the water conservancy system is used to obtain the regional scale and water area type distribution corresponding to the water conservancy system, and conduct sensing planning and layout for the water conservancy system based on the regional scale and water area type distribution to generate water conservancy system sensing planning and layout nodes; by deploying corresponding water quality sensors inside the water conservancy system sensing planning and layout nodes to form a distributed IoT sensing network, and using the distributed IoT sensing network to conduct real-time monitoring of the water quality index of the water conservancy system, so as to obtain the water quality index dataset of the water conservancy system, including the pH value of the water conservancy area, the dissolved oxygen concentration of the water conservancy area, the turbidity of the water conservancy area, the heavy metal concentration of the water conservancy area, and the microorganism concentration of the water conservancy area;

[0143] The regional index time step division module is used to couple the regional water quality index distribution of the water conservancy system based on the water quality index dataset of the water conservancy system to generate the regional water quality index distribution field of the water conservancy system; conduct regional time step division on the regional water quality index distribution field of the water conservancy system, so as to obtain the corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration, and water conservancy microorganism concentration in each time step of the water conservancy system area;

[0144] The regional microorganism water quality impact analysis module is used to evaluate the microbial growth efficiency of the water conservancy microorganism concentration corresponding to each time step in the water conservancy system area based on the water conservancy pH value, water conservancy dissolved oxygen concentration, and water conservancy heavy metal concentration corresponding to each time step in the water conservancy system area to obtain the water conservancy regional microbial growth impact efficiency; obtain the water body pollution distribution area corresponding to each time step in the water conservancy system area, and conduct microbial pollution attenuation analysis on the corresponding water conservancy microorganism concentration based on the water body pollution distribution area and water conservancy turbidity corresponding to each time step in the water conservancy system area, so as to obtain the water conservancy regional microbial pollution degradation and attenuation rate;

[0145] The regional water quality pollution anomaly management module is used to quantify the water quality pollution anomaly of the water quality index data set of the water conservancy system based on the influence efficiency of microbial growth in the water conservancy region and the degradation attenuation rate of microbial pollution in the water conservancy region, so as to obtain the degree of water quality pollution anomaly in the water conservancy system region; based on the degree of water quality pollution anomaly in the water conservancy system region, conduct anomaly category decision management on the corresponding regional water quality in the water conservancy system, generate a water quality anomaly category management strategy corresponding to the water conservancy system region, and execute the corresponding water quality category management work in the water conservancy system region.

[0146] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A water quality monitoring and analysis method for a water conservancy system, characterized in that: The following steps are involved: Step S1: obtaining the regional scale and water type distribution corresponding to the water conservancy system, and performing sensor planning and layout of the water conservancy system based on the regional scale and water type distribution to generate sensor planning and layout nodes of the water conservancy system; By deploying corresponding water quality sensors in the sensor planning and layout nodes of the water conservancy system to connect and generate a distributed IoT sensor network, and using the distributed IoT sensor network to monitor the water quality indicators of the water conservancy system in real time, a water quality indicator data set of the water conservancy system is obtained, including the pH value of the water conservancy area, the dissolved oxygen concentration of the water conservancy area, the turbidity of the water conservancy area, the heavy metal concentration of the water conservancy area, and the microbial concentration of the water conservancy area. Step S2: Based on the water quality index data set of the water conservancy system, the regional water quality index distribution of the water conservancy system is coupled to generate a regional water quality index distribution field of the water conservancy system; the regional water quality index distribution field of the water conservancy system is divided into regional time steps to obtain the corresponding water pH value, water dissolved oxygen concentration, water turbidity, water heavy metal concentration and water microorganism concentration of the water conservancy system region at each time step; Step S3: Based on the water pH value, water dissolved oxygen concentration and water heavy metal concentration corresponding to the water conservancy system area at each time step, the water conservancy microorganism concentration corresponding to the water conservancy system area at each time step is evaluated for microbial growth efficiency, and the microbial growth impact efficiency of the water conservancy area is obtained; the water pollution distribution area corresponding to the water conservancy system area at each time step is obtained, and based on the water pollution distribution area and water turbidity corresponding to the water conservancy system area at each time step, the corresponding water conservancy microorganism concentration is analyzed for microbial pollution attenuation, and the degradation attenuation rate of microbial pollution in the water conservancy area is obtained; Step S4: quantifying the water pollution anomaly of the water quality index data set of the water conservancy system based on the microbial growth influence efficiency of the water conservancy area and the microbial pollution degradation attenuation rate of the water conservancy area to obtain the degree of water pollution anomaly in the water conservancy system area; Based on the abnormal degree of water pollution in the water conservancy system area, abnormal category decision management is carried out on the corresponding regional water quality in the water conservancy system, and a water quality abnormal category management strategy corresponding to the water conservancy system area is generated to execute the corresponding water quality category management work in the water conservancy system area.

2. The water quality monitoring and analysis method of a water conservancy system according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining the regional scale and water area type distribution corresponding to the water conservancy system; Step S12: Based on the regional scale and water type distribution corresponding to the water conservancy system, a regional hydrodynamic sensitivity gradient analysis is performed on the water conservancy system, so as to analyze the corresponding regional hydrodynamic sensitivity gradient distribution by using computational fluid dynamics (CFD) to simulate the flow velocity, pressure and turbulence intensity of the water conservancy system under different terrain scales and water distributions, and generate a water conservancy regional hydrodynamic sensitivity gradient distribution field; Step S13: Perform sensor planning and layout for the hydrodynamic sensitive gradient distribution field in the water conservancy area, so as to set a comprehensive sensor monitoring node every 5-10 kilometers in the corresponding medium and high gradient distribution areas in the hydrodynamic sensitive gradient distribution field in the water conservancy area, so as to generate a sensor planning and layout node for the water conservancy system; Step S14: Deploy corresponding pH sensors, dissolved oxygen sensors, turbidity sensors, heavy metal sensors, and microbial monitoring sensors in each water conservancy system sensor planning and layout node and connect them to generate a distributed IoT sensor network; Step S15: Use the distributed IoT sensor network to monitor the water quality indicators of the water conservancy system in real time to obtain a water quality indicator data set of the water conservancy system, including the pH value of the water conservancy area, the dissolved oxygen concentration of the water conservancy area, the turbidity of the water conservancy area, the heavy metal concentration of the water conservancy area, and the microbial concentration of the water conservancy area.

3. The water quality monitoring and analysis method of a water conservancy system according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: obtaining the water conservancy geographical spatial position distribution and water conservancy topography elevation distribution corresponding to the water conservancy system; Step S22: performing a water conservancy spatial distribution simulation on the water conservancy system based on the water conservancy geographic spatial position distribution and the water conservancy topography elevation distribution to generate a water conservancy system regional spatial simulation distribution field; Step S23: performing spatiotemporal scale fluctuation analysis on each water quality index in the water quality index data set of the water conservancy system, and obtaining the spatiotemporal scale fluctuation distribution corresponding to each water quality index of the water conservancy system; Step S24: performing regional water quality index distribution coupling on the water conservancy system regional spatial simulation distribution field based on the spatiotemporal scale variation fluctuation distribution corresponding to each water quality index of each water conservancy system, so as to generate a water quality index distribution field for the water conservancy system region; Step S25: Divide the water quality index distribution field of the water conservancy system area into regional time steps to obtain the water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration and water conservancy microorganism concentration corresponding to the water conservancy system area at each time step.

4. The water quality monitoring and analysis method of a water conservancy system according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: obtaining the corresponding water conservancy microorganism growth and reproduction amount through the corresponding water conservancy microorganism concentration in the water conservancy system area at each time step; Step S32: Calculate the growth rate of the water conservancy microorganisms corresponding to the water conservancy system area at each time step to obtain the growth rate of the water conservancy microorganisms corresponding to the water conservancy system area at each time step; Step S33: Based on the water pH value, water dissolved oxygen concentration and water heavy metal concentration corresponding to the water system area at each time step, the water microbial growth rate corresponding to the water system area at each time step is evaluated to obtain the microbial growth efficiency of the water system area; Step S34: obtaining the water suspended matter distribution corresponding to the water conservancy system area at each time step, and quantifying the pollution distribution according to the water suspended matter distribution corresponding to the water conservancy system area at each time step, to obtain the water pollution distribution area corresponding to the water conservancy system area at each time step; Step S35: Based on the water pollution distribution area and water turbidity corresponding to the water conservancy system area at each time step, a microbial pollution attenuation analysis is performed on the corresponding water conservancy microbial concentration to obtain the microbial pollution degradation attenuation rate of the water conservancy area.

5. The water quality monitoring and analysis method of a water conservancy system according to claim 4, characterized in that: Step S33 includes the following steps: Based on the corresponding water conservancy pH value and water conservancy dissolved oxygen concentration of the water conservancy system area at each time step, the heavy metal redox influence analysis is carried out on the corresponding water conservancy heavy metal concentration, and the corresponding heavy metal redox influence coefficient of the water conservancy system area at each time step is obtained; According to the heavy metal redox state influence coefficient corresponding to the water conservancy system area at each time step, the heavy metal affinity of the growth substrate corresponding to the microbial growth process in the water conservancy system area is analyzed to obtain the heavy metal affinity constant of the microbial growth substrate corresponding to the water conservancy system area at each time step; Based on the water pH value, water dissolved oxygen concentration and heavy metal affinity constant of microbial growth substrate corresponding to the water conservancy system area at each time step, the microbial growth efficiency is evaluated by using the microbial growth impact calculation formula to obtain the microbial growth impact efficiency of the water conservancy area.

6. The water quality monitoring and analysis method of a water conservancy system according to claim 5, characterized in that: The microbial growth impact calculation formula is specifically: In the formula, μ s is the efficiency of microbial growth in water conservancy area, n is the total number corresponding to the time step, i is the item index corresponding to the time step, T i is the i-th time step, t is the time step variable parameter, μ(t) is the corresponding water conservancy microorganism growth rate in the water conservancy system area at time step t, S(t) is the corresponding water conservancy microorganism substrate concentration in the water conservancy system area at time step t, K S is the half-saturation constant of water conservancy microorganism substrate, O(t) is the water conservancy dissolved oxygen concentration corresponding to the water conservancy system area at time step t, K O is the half-saturation constant of dissolved oxygen in the water conservancy, exp is the exponential function, pH(t) is the water conservancy pH value corresponding to the water conservancy system area at time step t, α is the microbial growth inhibition coefficient, M(t) is the heavy metal concentration corresponding to the water conservancy system area at time step t, K M is the heavy metal half-saturation constant, ε is the heavy metal affinity constant of the microbial growth substrate, and η is the correction coefficient of the efficiency of microbial growth in the water conservancy area.

7. The water quality monitoring and analysis method of a water conservancy system according to claim 4, characterized in that: Step S35 includes the following steps: Step S351: performing a microbial pollution distribution attenuation simulation on the corresponding water conservancy microorganism concentration based on the water pollution distribution area and water conservancy turbidity corresponding to the water conservancy system area at each time step, so as to generate the microbial pollution distribution attenuation process corresponding to the water conservancy system area at each time step; Step S352: determining the attenuation half-life of the microbial pollution distribution attenuation process corresponding to the water conservancy system area at each time step, and obtaining the microbial pollution attenuation half-life corresponding to the water conservancy system area at each time step; Step S353: quantifying the pollution degradation attenuation of the water pollution distribution in the corresponding microbial pollution distribution attenuation process based on the microbial pollution attenuation half-life corresponding to the water conservancy system area at each time step, and obtaining the microbial water pollution degradation attenuation corresponding to the water conservancy system area at each time step; Step S354: Calculate the decay rate according to the corresponding microbial water pollution degradation decay amount in the water conservancy system area at each time step to obtain the microbial pollution degradation decay rate in the water conservancy area.

8. The water quality monitoring and analysis method of a water conservancy system according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: quantifying the water quality pollution anomaly of the water quality index data set of the water conservancy system based on the microbial growth influence efficiency of the water conservancy area and the degradation attenuation rate of microbial pollution in the water conservancy area using the regional water quality pollution anomaly calculation formula to obtain the degree of water quality pollution anomaly in the water conservancy system area; Step S42: based on the abnormal degree of water pollution in the water conservancy system area, the water quality of the corresponding area in the water conservancy system is classified into abnormal categories, and the abnormal water quality categories corresponding to the water conservancy system area are generated, including abnormal water quality category I, abnormal water quality category II and abnormal water quality category III; Step S43: Perform abnormal category decision management on the corresponding regional water quality in the water conservancy system according to the water quality abnormal category corresponding to the water conservancy system area, generate a water quality abnormal category management strategy corresponding to the water conservancy system area, and execute the corresponding water quality category management work in the water conservancy system area.

9. The water quality monitoring and analysis method of a water conservancy system according to claim 8, characterized in that: The specific calculation formula of regional water pollution anomaly in step S41 is: Where P is the abnormal degree of water pollution in the water conservancy system area, A is the size of the water conservancy system area, x is the horizontal coordinate of the water conservancy system area location point, y is the vertical coordinate of the water conservancy system area location point, pH(x,y) is the water pH value of the water conservancy system area at the location point (x,y), O(x,y) is the water dissolved oxygen concentration of the water conservancy system area at the location point (x,y), Z(x,y) is the water turbidity of the water conservancy system area at the location point (x,y), M(x,y) is the water heavy metal concentration of the water conservancy system area at the location point (x,y), C(x,y) is the water microbial concentration of the water conservancy system area at the location point (x,y), μ s The growth efficiency of microorganisms in water conservancy areas, δ v is the degradation attenuation rate of microbial pollution in the water conservancy area, and ξ is the correction coefficient of the abnormal degree of water quality pollution in the water conservancy system area.

10. A water quality monitoring and analysis system for a water conservancy system, characterized in that: Used to execute the water quality monitoring and analysis method of the water conservancy system as claimed in claim 1, the water quality monitoring and analysis system of the water conservancy system comprises: The water quality index monitoring module of the water conservancy system is used to obtain the regional scale and water type distribution corresponding to the water conservancy system, and to plan and deploy the water conservancy system based on the regional scale and water type distribution to generate the water conservancy system sensor planning and deployment nodes; by deploying the corresponding water quality sensors in the water conservancy system sensor planning and deployment nodes to connect and generate a distributed IoT sensor network, and using the distributed IoT sensor network to monitor the water quality indicators of the water conservancy system in real time, so as to obtain the water quality index data set of the water conservancy system, including the pH value of the water conservancy area, the dissolved oxygen concentration of the water conservancy area, the turbidity of the water conservancy area, the heavy metal concentration of the water conservancy area, and the microbial concentration of the water conservancy area; The regional indicator time step division module is used to couple the regional water quality indicator distribution of the water conservancy system based on the water quality indicator data set of the water conservancy system to generate the regional water quality indicator distribution field of the water conservancy system; the regional water quality indicator distribution field of the water conservancy system is divided into regional time steps to obtain the corresponding water pH value, water dissolved oxygen concentration, water turbidity, water heavy metal concentration and water microorganism concentration of the water conservancy system region at each time step; The regional microbial water quality impact analysis module is used to evaluate the microbial growth efficiency of the water conservancy microorganism concentration corresponding to the water conservancy system area at each time step based on the water conservancy pH value, water conservancy dissolved oxygen concentration and water conservancy heavy metal concentration corresponding to the water conservancy system area at each time step, and obtain the microbial growth impact efficiency of the water conservancy area; obtain the water pollution distribution area corresponding to the water conservancy system area at each time step, and perform microbial pollution attenuation analysis on the corresponding water conservancy microorganism concentration based on the water pollution distribution area and water conservancy turbidity corresponding to the water conservancy system area at each time step, so as to obtain the degradation attenuation rate of microbial pollution in the water conservancy area; The regional water quality pollution anomaly management module is used to quantify the water quality pollution anomaly of the water quality index data set of the water conservancy system based on the influence efficiency of microbial growth in the water conservancy area and the degradation attenuation rate of microbial pollution in the water conservancy area, so as to obtain the degree of water quality pollution anomaly in the water conservancy system area; based on the degree of water quality pollution anomaly in the water conservancy system area, the corresponding regional water quality in the water conservancy system is managed by abnormal category decision, and the corresponding water quality anomaly category management strategy of the water conservancy system area is generated to perform the corresponding water quality category management work in the water conservancy system area.

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