A method and system for evaluating and predicting the health status of the aquatic ecological environment

Abstract

The present invention relates to the field of environmental assessment, and discloses a method and system for assessing and predicting the health status of the aquatic ecological environment. The method comprises the following steps: acquiring and preprocessing the basic data of the aquatic ecological environment; constructing a water quality change model and an emergency impact model; calculating the time series data of the concentrations of various water quality parameters and the numbers of various species; constructing a multi-objective optimization model and solving it to obtain the optimal management strategy parameters. The present invention can accurately simulate the impact of aquatic ecological environment emergencies, optimize the assessment indicators, and simultaneously consider multiple key factors such as the interaction of microscopic biological communities, spatial heterogeneity, and the uncertainty of emergencies, and has strong practicability and adaptability.

Description

Technical Field

[0001] The present invention relates to the field of environmental assessment, and more specifically, to a method and system for assessing and predicting the health status of aquatic ecological environment. Background Art

[0002] The assessment and prediction of the health status of the water ecological environment is of great significance for water resources management and ecological protection. Traditional water ecological environment assessment methods mainly rely on manual sampling and laboratory analysis, which have problems such as long assessment cycle, poor timeliness, and weak prediction ability. In particular, when a sudden pollution incident occurs in the water ecological environment, how to quickly assess the impact of the pollution incident on the water ecosystem and timely predict the changing trend of the ecological health status is crucial for emergency response and decision-making, but it is difficult to achieve with existing technical means.

[0003] Therefore, there is an urgent need for a new method that can realize the rapid assessment and dynamic prediction of the health status of the water ecological environment, so as to improve the timeliness and scientificity of water ecological environment supervision and provide strong support for the emergency management of sudden events. Summary of the invention

[0004] The present invention provides a method and system for assessing and predicting the health status of aquatic ecological environment. By introducing machine learning algorithms and data-driven models, rapid assessment and dynamic prediction of the health status of water ecology can be achieved, and a real-time early warning and decision-making optimization mechanism can be established to improve the level of water ecological environment supervision and emergency management.

[0005] A method for assessing and predicting the health status of aquatic ecological environment comprises the following steps:

[0006] Acquire and preprocess basic data of water ecological environment, including water quality parameters, hydrological parameters and aquatic biological data;

[0007] Among them, water quality parameters include dissolved oxygen content , Chemical Oxygen Demand , ammonia nitrogen content ;

[0008] Hydrological parameters include water velocity , water area ;

[0009] Aquatic data including the number of species ;

[0010] Modeling water quality changes:

[0011] ;

[0012] in for Water concentration at all times, is the initial concentration, is the reaction rate constant, is the equilibrium concentration;

[0013] Construct an emergency impact model:

[0014]

[0015] where is the pollutant release amount, is the mapping function between the event type and the pollutant release amount;

[0016] Simulate and run, set the simulation time interval and the time step , and calculate the time series data of the concentrations of each water quality parameter and the number of each species for each time step;

[0017] Index optimization, define the management strategy parameter vector , construct a water quality objective function and a biodiversity objective function, and use a multi-objective optimization algorithm to solve the optimal management strategy parameters.

[0018] Preferably: in the emergency impact model: when is a chemical leak, ; when is a biological invasion, ; where is the initial release amount (mg / s), is the decay coefficient (1 / s), is the base of the natural logarithm, represents the exponential function.

[0019] Preferably: also include a microscopic biological community interaction model:

[0020] ;

[0021] where is the intrinsic growth rate of organism , is the environmental carrying capacity, is the interaction coefficient between organism and organism , and are the numbers of organism and organism respectively, represents the sum over all other species except species , where is the total number of species.

[0022] Preferably: The water body is divided into spatial units. For the spatial unit , its water quality concentration change model is:

[0023] ;

[0024] where is the mass exchange coefficient between unit and unit , is the total number of spatial units, denotes summation over all adjacent units except unit for calculating the total mass exchange between spatial units.

[0025] Preferably: The pollutant release amount follows a normal distribution . Multiple simulations are carried out using the Monte Carlo method. The optimization objective is:

[0026] ;

[0027] where is the mathematical expectation of the objective function, is the variance of the objective function, is the trade-off coefficient.

[0028] Preferably: It further includes a real-time data update step:

[0029] The water quality concentration data is updated using a dynamic update algorithm:

[0030] ;

[0031] where is the updated water quality concentration, is the water quality concentration before update, is the newly measured water quality concentration, is the update weight.

[0032] A water ecological environment health status assessment and prediction system includes: a data acquisition module for obtaining basic data of the water ecological environment; a model construction module for constructing a water quality change model and an emergency impact model; a simulation calculation module for calculating time series data; and an optimization analysis module for solving optimal management strategy parameters.

[0033] It further includes:

[0034] a real-time monitoring module for collecting real-time water quality data; a data update module for dynamically updating model parameters; and an early warning decision module for generating early warning information and management suggestions.​​

[0035] The simulation calculation module further includes: a Monte Carlo simulation unit for performing multiple random simulations; a spatial analysis unit for calculating water quality changes in different spatial units; and a biological interaction unit for simulating the dynamics of microscopic biological communities.

[0036] The optimization analysis module uses the non-dominated sorting genetic algorithm (NSGA-II) to perform multi-objective optimization and solution.

[0037] The beneficial effects of the present invention are as follows: The present invention can accurately simulate the impact of water ecological environment emergencies, optimize evaluation indicators, and simultaneously consider multiple key factors such as the interaction of microscopic biological communities, spatial heterogeneity, and the uncertainty of emergencies, and has strong practicability and adaptability.

[0038] The present invention proposes a method for evaluating and predicting the health status of water ecological environment based on machine learning and data-driven models, overcomes the limitations of long evaluation period and weak prediction ability of traditional methods, and realizes the rapid diagnosis and trend prediction of water ecological health status.

[0039] An ecological health evaluation model driven by multi-source heterogeneous data fusion is constructed, new evaluation indicators such as plankton biodiversity index are introduced, the evaluation dimension is expanded, and the comprehensiveness and accuracy of health status characterization are improved.

[0040] A water ecological health prediction model for emergencies is developed, which can simulate dynamic processes such as pollutant diffusion and biological damage, realize the rolling prediction of health status after the event, and provide a scientific basis for emergency decision-making.

[0041] Functions such as real-time warning, scenario analysis, and countermeasure optimization are integrated, the practicability of the system is enhanced, and it can provide intelligent technical means for the management of basin water ecological environment and improve management efficiency. Brief Description of the Drawings

[0042] Figure 1 is a flowchart of a method for evaluating and predicting the health status of water ecological environment proposed by the present invention;

[0043] Figure 2 is an example diagram of the basic data of the water ecological environment in Embodiment 1 of the present invention;

[0044] Figure 3 is an example diagram of the emergency parameters in Embodiment 1 of the present invention;

[0045] Figure 4 is an example diagram of the model parameter settings in Embodiment 1 of the present invention;

[0046] Figure 5 is an example diagram of the simulation results in Embodiment 1 of the present invention;

[0047] Figure 6 It is an example diagram of the optimization result in Embodiment 1 of the present invention. Detailed implementation manners

[0048] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein, and changes can be made to the functions and arrangements of the elements discussed without departing from the scope of protection of the content of this specification. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described relative to some examples can also be combined in other examples.

[0049] Embodiment 1

[0050] This embodiment provides a method for evaluating and predicting the health status of the water ecological environment, including the following steps:

[0051] Step 100: Obtain and preprocess the basic data of the water ecological environment:

[0052] (1) The basic data of the water ecological environment includes:

[0053] Water quality parameters: Dissolved oxygen content (mg / L), Chemical oxygen demand ( ), Ammonia nitrogen content (mg / L);

[0054] Hydrological parameters: Water flow velocity (m / s), Water body area (m²);

[0055] Aquatic organism data: The number of each species (individuals / m³), where represents the species number;

[0056] (2) Historical data of emergencies, including the type of event , Occurrence time , Affected range (m).

[0057] Step 200: Construct a water quality change model and an emergency impact model:

[0058] (1) Construct a water quality change model:

[0059] ;

[0060] Wherein: is in the water quality change model Water quality concentration at a certain moment (mg / L);

[0061] is the initial concentration (mg / L);

[0062] is the reaction rate constant (1 / s);

[0063] is the equilibrium concentration (mg / L);

[0064] represents the definite integral from 0 to the moment t;

[0065] is the water quality concentration at the moment (mg / L), is the integration variable, representing any moment between 0 and t;

[0066] is the differential of the integration variable.

[0067] (2) Construct an impact model for emergencies:

[0068]

[0069] Among them: is the pollutant release rate (mg / s);

[0070] is the mapping function between the event type and the pollutant release rate, specifically:

[0071] When is a chemical leakage, ; When is a biological invasion, ;

[0072] Among them is the base of the natural logarithm, represents the exponential function;

[0073] Among them is the initial release rate (mg / s), is the decay coefficient (1 / s).

[0074] Step 300: Set the simulation time interval and time step, and calculate the time series data of each water quality parameter concentration and each species quantity for each time step:

[0075] Set the simulation time interval , time step ;

[0076] Among them: is the simulated termination time (s). is the time step (s).

[0077] For the i-th time step , calculate the dissolved oxygen content, chemical oxygen demand, and ammonia nitrogen content at the i-th time step, which are , , respectively. The number of each species at the i-th time step, and the number of the i-th species is ;

[0078] Record the time series data of the above parameters as the response data of the evaluation index.

[0079] Step 400: Define the management strategy parameter vector, construct the water quality objective function and the biodiversity objective function, and use the multi-objective optimization algorithm to solve the optimal management strategy parameters:

[0080] (1) Define the management strategy parameter vector:

[0081] ;

[0082] Where: is the pollutant emission limit (mg / s);

[0083] is the intensity of ecological restoration measures (dimensionless).

[0084] (2) Construct the objective function:

[0085] Water quality objective function:

[0086] ;

[0087] Biodiversity objective function:

[0088] ;

[0089] Where: represents the summation over all spatial units;

[0090] represents the definite integral from 0 to time T;

[0091] is the differential of time;

[0092] is the dissolved oxygen concentration (mg / L) of unit ;

[0093] is the dissolved oxygen standard value (mg / L);

[0094] Indicates summation over all species where is the total number of species;

[0095] is the number of species in the spatial unit at time T.

[0096] (3) Establish a multi-objective optimization model:

[0097] ;

[0098] Constraints: ;

[0099] .

[0100] where: is the maximum allowable pollutant emission limit (mg / s).

[0101] (4) Solve using the non-dominated sorting genetic algorithm (NSGA-II) to obtain the optimal management strategy parameters .

[0102] where: represents the optimal management strategy parameter vector, represents the optimal pollutant emission limit (mg / s), represents the intensity of the optimal ecological restoration measure (dimensionless), and the superscript represents the optimal solution.

[0103] To further illustrate the specific application effect of this embodiment, the following gives an actual application example of a chemical leakage incident in a certain river section:

[0104] See Appendix Figure 2 -Appendix Figure 6 , and it can be seen from the actual application data that:

[0105] 1. The chemical leakage incident caused a significant decrease in the dissolved oxygen in the water body, with the lowest dropping to 3.5 mg / L. At the same time, the chemical oxygen demand and ammonia nitrogen content increased significantly;

[0106] 2. The number of aquatic organisms (taking carp as an example) showed an obvious downward trend under the influence of the incident;

[0107] 3. Through the optimization method of the present invention, the pollutant emission limit was reduced from 60 mg / s to 35 mg / s, and at the same time, the intensity of the ecological restoration measure was increased to 0.7, resulting in a significant improvement in the water quality condition and the biological survival rate after 24 hours.

[0108] Example 2

[0109] Based on Embodiment 1, this embodiment is improved by additionally considering the interactions among microscopic biological communities. The main improvements are as follows:

[0110] In the data preprocessing of Step 100, obtain the interaction coefficients, intrinsic growth rates, and environmental carrying capacities .

[0111] In the model construction of Step 200, add a microscopic biological community interaction model and a coupled water quality change model.

[0112] The microscopic biological community interaction model is:

[0113] ;

[0114] Where: is the quantity of organism ;

[0115] is the intrinsic growth rate of organism , is the environmental carrying capacity, is the interaction coefficient between organism and organism ;

[0116] represents the rate of change of the quantity of species with time;

[0117] represents the summation over all other species except species for calculating the total effect of interactions among all species;

[0118] is the quantity of organism ;

[0119] represents the product of the quantities of organisms and organism for quantifying the intensity of interaction between two species.

[0120] The coupled water quality change model is:

[0121] ;

[0122] Where: is the water quality concentration (mg / L) at time in the coupled water quality change model, is the initial concentration (mg / L), is the reaction rate constant (1 / s), is the equilibrium concentration (mg / L), is the species influence coefficient on water quality, is the total number of species, represents the summation over all species.

[0123] The remaining steps are the same as in Example 1.

[0124] By introducing a microscopic biotic community interaction model, this example has the following advantages:

[0125] It more accurately reflects the complex relationships such as competition and symbiosis among biological populations in the water ecosystem, improving the ecological authenticity of the simulation;

[0126] By coupling with the water quality change model, it can simultaneously consider the impact of the biological community on water quality and the feedback effect of water quality change on the biological community, achieving a two-way coupled simulation of water quality - biology;

[0127] It helps to better predict the chain reaction of emergencies on the entire ecosystem, providing a more comprehensive basis for formulating ecological restoration measures.

[0128] Example 3

[0129] Based on Example 1, this example is improved by additionally considering spatial heterogeneity, and the main improvements are as follows:

[0130] The water body is divided into spatial units, and the area of each unit is , satisfying .

[0131] Among them: is the area (m²) of the th spatial unit, is the total area of the water body (m²), is the total number of spatial units, represents the summation over all spatial units.

[0132] When constructing the model, it is improved to a spatially distributed model. For the spatial unit , its water quality concentration change model is:

[0133] ;

[0134] Among them: is the spatial unit at Water quality concentration at a moment (mg / L);

[0135] is the spatial unit of the initial concentration (mg / L);

[0136] is the spatial unit of the reaction rate constant (1 / s);

[0137] is the spatial unit of the equilibrium concentration (mg / L);

[0138] represents the definite integral from 0 to the moment t;

[0139] is the spatial unit at the moment of the water quality concentration (mg / L), is the integration variable; is the differential of the integration variable;

[0140] is the unit and the unit between the mass transfer coefficient;

[0141] represents all adjacent units except the unit outside for summing to calculate the total mass transfer between spatial units, where is the total number of spatial units;

[0142] is the spatial unit at the moment of the water quality concentration (mg / L);

[0143] represents the concentration difference between the spatial unit and the spatial unit for calculating the driving force of mass diffusion.

[0144] During the simulation run, each spatial unit is calculated for each index separately.

[0145] The optimization objective function is improved to:

[0146] ;

[0147] ;

[0148] Among them: represents all Sum over all spatial units;

[0149] Denote the definite integral from time 0 to T;

[0150] Is the time differential;

[0151] Is the unit Dissolved oxygen concentration (mg / L);

[0152] Is the standard value of dissolved oxygen (mg / L);

[0153] Denote the sum over all species Where Is the total number of species;

[0154] Is the number of species In spatial unit At time T.

[0155] By introducing the consideration of spatial heterogeneity, this embodiment has the following advantages:

[0156] Overcome the limitation of treating the entire water body as a homogeneous system, and can reflect the differences in water quality and biological distribution at different spatial locations;

[0157] By considering the mass exchange between spatial units, more accurately simulate the diffusion process of pollutants and the spatial variation characteristics of water quality;

[0158] The optimization objective function takes into account the indicators of all spatial units, making the formulation of management strategies more spatially targeted and improving the treatment effect.

[0159] Example 4

[0160] Based on Example 1, this embodiment is improved by adding the consideration of the uncertainty of emergencies. The main improvements are as follows:

[0161] Data preprocessing: Determine the probability distribution parameters of the pollutant release amount :

[0162] ;

[0163] Where: Denote following a certain distribution; Denote the normal distribution with mean And variance

[0164] ​During the simulation run, the Monte Carlo method is used for multiple simulations to obtain the statistical characteristics of the evaluation metrics.

[0165] The optimization objective is improved to a robust optimization model:

[0166] ;

[0167] Where: represents minimizing the objective function;

[0168] represents the objective function 's mathematical expectation, used to measure the average performance;

[0169] represents the objective function 's variance, used to measure the degree of performance fluctuation;

[0170] is the trade-off coefficient, used to balance the importance of the expected value and variance.

[0171] By introducing the consideration of the uncertainty of emergencies, this embodiment has the following advantages:

[0172] It gets rid of the limitations of traditional deterministic models and can handle the random fluctuations of emergency parameters;

[0173] By obtaining the statistical characteristics of the evaluation metrics through Monte Carlo simulation, it provides more comprehensive risk assessment information;

[0174] Using the robust optimization method, the optimized management strategy has stronger anti-interference ability and can handle various uncertain situations.

[0175] Embodiment 5

[0176] Based on Embodiment 1, this embodiment adds a real-time data update function for improvement. The main improvements are as follows:

[0177] Add a real-time data acquisition and preprocessing module to obtain new measurement data .

[0178] Where: is the newly collected environmental monitoring data set, including the latest water quality parameters, hydrological parameters, and aquatic organism data.

[0179] Use a dynamic update algorithm to update the model parameters. For water quality concentration data:

[0180] ;

[0181] Where: is the updated water quality concentration (mg / L);

[0182] is the water quality concentration before updating (mg / L);

[0183] is the newly measured water quality concentration (mg / L);

[0184] is the update weight (a real number between 0 and 1).

[0185] Rerun the simulation using the updated parameters.

[0186] Re-calculate the optimization based on the updated simulation results

[0187] By introducing the real-time data update function, this embodiment has the following advantages:

[0188] The dynamic adjustment of model parameters is realized, so that the simulation results can reflect the actual changes of the water ecological environment in a timely manner;

[0189] The weighted update algorithm not only maintains the stability of the model but also ensures sensitive response to new data;

[0190] The accuracy and timeliness of model predictions are improved, providing more reliable decision-making support for emergency response to emergencies.

[0191] Example 6

[0192] In this embodiment, a water ecological environment health status assessment and prediction system is proposed, which is used to implement the methods in Embodiments 1 to 5, including:

[0193] The data acquisition module is used to obtain basic data on the water ecological environment; the model building module is used to build water quality change models and emergency impact models; the simulation calculation module is used to calculate time series data; and the optimization analysis module is used to solve the optimal management strategy parameters.

[0194] Also includes:

[0195] The real-time monitoring module is used to collect real-time water quality data; the data update module is used to dynamically update model parameters; and the early warning decision module is used to generate early warning information and management recommendations.

[0196] The simulation calculation module also includes: a Monte Carlo simulation unit for performing multiple random simulations; a spatial analysis unit for calculating water quality changes in different spatial units; and a biological interaction unit for simulating the dynamics of microscopic biological communities.

[0197] The optimization analysis module uses the non-dominated sorting genetic algorithm (NSGA-II) to solve multi-objective optimization problems.

[0198] The embodiments of the present invention have been described above. However, these embodiments are not limited to the specific implementation manners described above. The above specific implementation manners are merely illustrative rather than restrictive. Under the inspiration of these embodiments, those of ordinary skill in the art can also make many forms, all of which fall within the protection scope of these embodiments.

Claims

1. A method for assessing and predicting the health status of aquatic ecological environment, characterized in that: The steps include: Acquire and preprocess basic data of water ecological environment, including water quality parameters, hydrological parameters and aquatic biological data; Modeling water quality changes: ； in for Water concentration at all times, is the initial concentration, is the reaction rate constant, is the equilibrium concentration, represents the definite integral from 0 to time t; for The water concentration at the time, is the integral variable, representing any time between 0 and t; Constructing the impact model of emergencies: ； in is the amount of pollutant released, is the mapping function between event type and pollutant release amount; Simulate and run, set simulation time interval and time step , calculate the time series data of each water quality parameter concentration and the number of each species for each time step; Indicator optimization, definition of management strategy parameter vector , construct water quality objective function and biodiversity objective function, and use multi-objective optimization algorithm to solve the optimal management strategy parameters; In the emergency impact model: when In case of chemical spills, ; when When biological invasion occurs, ; in is the initial release amount, is the attenuation coefficient, is the base of natural logarithms, represents the exponential function; Also included are models for microbial community interactions: ； in: For Biology the number of For Biology The intrinsic growth rate, is the environmental carrying capacity, For Biology and Biological The interaction coefficient between Indicates species The rate of change of the quantity over time; Indicates that except for species All other species The sum was used to calculate the total effect of all interactions among species; For Biology the number of Represents biological and Biological The product of quantities is used to quantify the strength of the interaction between two species.

2. A method for assessing and predicting the health status of aquatic ecological environment according to claim 1, characterized in that: Divide the water into Space units, for space units , and its water quality concentration change model is: ； in: For space unit exist Water concentration at the time; For space unit The initial concentration of For space unit The reaction rate constant; For space unit The equilibrium concentration of represents the definite integral from 0 to time t; For space unit exist The water concentration at the time, is the integration variable; is the differential of the integrated variable; For unit and unit The material exchange coefficient between Indicates that except for the unit All adjacent units except The sum is used to calculate the total amount of material exchange between space units, where is the total number of spatial units; For space unit exist Water concentration at the time; Represents spatial unit With space unit The concentration difference between them is used to calculate the driving force for the diffusion of substances.

3. A method for assessing and predicting the health status of aquatic ecological environment according to claim 1, characterized in that: Pollutant release Normal distribution , Monte Carlo method is used for multiple simulations, and the optimization goal is: ； in represents the minimization objective function; Represents the objective function The mathematical expectation of , which is used to measure average performance; Represents the objective function The variance is used to measure the degree of performance fluctuation; is a trade-off coefficient used to balance the importance of expected value and variance.

4. A method for assessing and predicting the health status of aquatic ecological environment according to claim 1, characterized in that: Also includes real-time data update steps: Use dynamic update algorithm to update water quality concentration data: ； in is the updated water quality concentration, is the water quality concentration before updating, is the newly measured water quality concentration, To update the weights.

5. A water ecological environment health status assessment and prediction system, characterized in that: It is used to execute a method for assessing and predicting the health status of a water ecological environment as claimed in any one of claims 1 to 4, comprising: a data acquisition module for acquiring basic data on the water ecological environment; a model building module for building a water quality change model and an emergency impact model; a simulation calculation module for calculating time series data; and an optimization analysis module for solving optimal management strategy parameters.

6. A water ecological environment health status assessment and prediction system according to claim 5, characterized in that: Also includes: Real-time monitoring module, used to collect real-time water quality data; Data update module, used to dynamically update model parameters; The early warning decision module is used to generate early warning information and management suggestions.

7. A water ecological environment health status assessment and prediction system according to claim 6, characterized in that: The simulation calculation module also includes: a Monte Carlo simulation unit for performing multiple random simulations; a spatial analysis unit for calculating water quality changes in different spatial units; and a biological interaction unit for simulating the dynamics of microscopic biological communities.

8. A water ecological environment health status assessment and prediction system according to claim 5, characterized in that: The optimization analysis module uses a non-dominated sorting genetic algorithm to solve multi-objective optimization problems.

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