A method for predicting marine organisms in the sea area around a nuclear power plant

Abstract

The present invention discloses a method for predicting marine organisms in the sea area around a nuclear power plant, which includes monitoring the hydrology and water quality of the target sea area and investigating and evaluating the marine biological resources; establishing a three-dimensional hydrodynamic model of the target sea area based on the monitoring data of the hydrology and water quality; interpolating the obtained marine biomass and the corresponding outbreak location information into different moments of the three-dimensional hydrodynamic model to establish a biological drift numerical model, and using the biological drift numerical model to simulate and predict the migration path of the marine organisms. The method for predicting marine organisms in the sea area around a nuclear power plant provided by the present invention utilizes the typical habits of typical marine organisms in the cold source sea area of the nuclear power plant, obtains the basic characteristics of marine ecological dynamics changes in this sea area, and realizes the prediction of the movement trajectories of marine organisms.

Description

Technical Field

[0001] The present invention relates to the technical field of marine biological prediction, and particularly to a method for predicting marine organisms in the sea area around a nuclear power plant. Background Art

[0002] The water intake of coastal nuclear power plants is extremely prone to blockage, which seriously affects the operation safety of nuclear power plants. Once it occurs, it will not only cause significant power and economic losses, but also threaten the safety of the nuclear power plants themselves. Therefore, it is particularly important to monitor and prevent the high-risk outbreak of marine organisms in the cold source water intake area of nuclear power plants. Among such marine organisms at risk of cold source water intake, organisms such as algae (such as large green algae), jellyfish (such as moon jellyfish), and fish and shrimp (such as Acetes) need to be key monitored and warned.

[0003] Artificial pole culture facilities will also form spawning grounds with good shelter, and the survival rates of fish eggs and larvae will be higher than those in general sea areas. Such marine organisms with low swimming ability will enter the water intake of nuclear power plants with the tide, seriously threatening the current situation of cold source safety.

[0004] Domain experts attach great importance to this and propose that the cold source problem should be solved from the source and at the fundamental design level to ensure the long-term safety of the cold source of the power plant. Studying the impact of marine organisms and foreign objects on the water intake safety of nuclear power plants has great value and significance. Summary of the Invention

[0005] In order to overcome the deficiencies of the prior art, the present invention provides a method for predicting marine organisms in the sea area around a nuclear power plant. The specific technical solutions are as follows:

[0006] A method for predicting marine organisms in the sea area around a nuclear power plant is provided, including the following steps:

[0007] S1. Conduct hydrological and water quality monitoring and marine biological resource investigation and evaluation on the target sea area;

[0008] S2. Establish a three-dimensional hydrodynamic model of the target sea area based on the monitoring data of hydrology and water quality;

[0009] S3. Interpolate the investigated marine biomass and corresponding outbreak location information into different moments of the three-dimensional hydrodynamic model to establish a biological drift numerical model, and use the biological drift numerical model to simulate and predict the migration path of the marine organisms.

[0010] Further, before step S1, it is necessary to first determine the investigation scope, investigation time and frequency of the target sea area, and conduct the investigation in a combined manner of continuous observation, large-scale observation and underway observation.

[0011] Furthermore, the hydrological and water quality monitoring includes continuous observations of the tidal level and flow rate at the water intake of the nuclear power plant, large-scale observations based on hydro-meteorological elements, large-scale observations based on water chemical elements, and underway observations using an acoustic Doppler current profiler.

[0012] Furthermore, in step S2, the three-dimensional hydrodynamic model adopts ROMS, SCHISM or FVCOM.

[0013] Furthermore, in step S3, by assimilating the results of real-time monitoring and investigation, calculate and correct the probability of the trajectory backtracking and destination distribution of marine organisms, and use the locations of marine organisms obtained from real-time monitoring as the initial points and correction points to continuously correct the trajectory parameters, so as to optimize the prediction of the migration paths of marine organisms.

[0014] Furthermore, based on the marine biological resources survey data, evaluate the monthly / quarterly biomass and succession patterns of marine organisms, combine with the historical data of the target sea area, evaluate the interannual variation patterns of marine organisms in the risk calendar, and through the analysis of the biological habits of marine organisms in the target sea area and their correlation with the environment, obtain the biomass changes and species succession patterns of marine organisms in the target sea area, and optimize the cold source risk identification calendar to achieve the prediction of the outbreak timing of marine organisms in the target sea area.

[0015] Furthermore, according to the nutrient and marine organism related data of the target sea area, simulate the flux changes of a single nutrient element among the state variables of organic mass, and then further analyze using the environmental state variables to obtain a support model of nutrients for the entire food web based on time series, as the nutrient support model of the target sea area;

[0016] Based on the allometric measurement theory of ecology, calculate the physiological losses caused by the allometric relationship based on the metabolic theory and the ecological efficiency function of the losses and gains caused by predation through the changes in individual-level data of marine organisms, so as to obtain the proportion of energy used by marine organisms for production; describe the energy flux between species or trophic groups, and use the food web method to link community and ecosystem functions, and based on the matrix of energy flow between species, simulate the future direction of energy flow in the target sea area to construct an energy flow model;

[0017] According to the research results of C and N stable isotopes of various producer and consumer species in the target sea area, establish a Bayesian-based stable isotope mixing model to determine the trophic levels of species in the target sea area and their predation and trophic relationships, so as to construct dynamic equations of producer and consumer species in the ecosystem of the target sea area, and thus generate a food web model;

[0018] Combine the nutrient support model, the energy flow model, and the food web model to obtain a biological outbreak prediction model for outputting the prediction results of specified organisms.

[0019] Furthermore, classify the marine organisms in the target sea area. The classification influencing factors include the biological characteristics of the organisms and the potential clogging severity. The biological characteristics of the organisms involve environmental adaptability, reproduction methods and capabilities, natural enemy distribution, and the ability to occupy habitats. The potential clogging severity involves individual size, the possibility of biological aggregation, floating property, and the number of individuals. Use the multi-level fuzzy comprehensive evaluation model and conduct qualitative grading and assignment to form a comprehensive risk assessment model to determine the disaster-causing organisms that have an impact on the intake pipeline of the nuclear power plant.

[0020] Furthermore, the comprehensive risk assessment model uses the following calculation formula for judgment.

[0021] D = C × B

[0022] Where D is the disaster-causing risk index, C is the biological characteristic index, and B is the clogging severity index;

[0023]

[0024] Where i is the index factor, and C i is the grading membership assignment of the biological characteristic index factor i;

[0025]

[0026] Where b i is the grading membership assignment of the possibility index factor i, and W Bi is the weight of the disaster-causing index factor i;

[0027] The comprehensive risk assessment model classifies marine organisms into different hazard levels according to different disaster-causing risk indexes.

[0028] Furthermore, use the output results of the biological outbreak prediction model and the comprehensive risk assessment model to obtain the prediction of the risk degree of marine organisms in the target sea area to the water intake of the nuclear power plant, and give corresponding early warning prompts.

[0029] Compared with the prior art, the present invention has the following advantages: Utilize the typical habits and laws of marine organisms in the cold source sea area of the nuclear power plant, obtain the basic characteristics of the dynamic changes of the marine ecosystem in this sea area, and realize the prediction of the movement trajectories of marine organisms. Brief Description of the Drawings

[0030] Figure 1 It is a schematic flow chart of the method for predicting marine organisms in the sea area around the nuclear power plant provided by the embodiment of the present invention;

[0031] Figure 2 It is a schematic diagram of the warning platform framework in the method for predicting marine organisms in the sea area around a nuclear power plant provided by an embodiment of the present invention;

[0032] Figure 3 It is a schematic diagram of the network architecture of the warning platform in the method for predicting marine organisms in the sea area around a nuclear power plant provided by an embodiment of the present invention. Detailed implementation manners

[0033] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion.

[0035] In an embodiment of the present invention, a method for predicting marine organisms in the sea area around a nuclear power plant is provided. Refer to Figure 1 , including the following steps:

[0036] S1. Conduct hydrological and water quality monitoring and marine biological resource investigation and evaluation on the target sea area;

[0037] Before that, it is necessary to first determine the investigation scope, investigation time and frequency of the target sea area, and conduct the investigation by combining continuous observation, large-area observation and underway observation.

[0038] Use the established monitoring network to collect and analyze data. In the cold source water intake of the nuclear power plant and the surrounding sea area, conduct annual monitoring on marine dynamic elements including seawater quality, organisms, hydro-meteorological elements, and tidal level and current, obtain the basic marine ecological dynamics change characteristics of this sea area, and grasp the change activity rules of cold source organisms.

[0039] S2. Establish a three-dimensional hydrodynamic model of the target sea area based on the monitoring data of hydrology and water quality;

[0040] Among them, the three-dimensional hydrodynamic model adopts ROMS, SCHISM or FVCOM.

[0041] S3. Interpolate the investigated marine biomass and corresponding outbreak location information into different time instances of the three-dimensional hydrodynamic model to establish a biological drift numerical model, and use the biological drift numerical model to simulate and predict the migration paths of the marine organisms;

[0042] Among them, by assimilating the real-time monitoring survey results, calculate and correct the trajectory backtracking and destination distribution probability of marine organisms, and use the real-time monitored locations of marine organisms as the initial points and correction points to continuously correct the trajectory parameters to achieve the prediction optimization of the migration paths of marine organisms.

[0043] Based on the marine organism resource survey data, evaluate the monthly / quarterly biomass and succession rules of marine organisms, and combine with the historical data of the target sea area to evaluate the inter-annual variation rules of marine organisms in the risk calendar. Through the analysis of the biological habits of marine organisms and their correlation with the environment in the target sea area, obtain the biomass change and species succession rules of marine organisms in the target sea area, and optimize the cold source risk identification calendar to achieve the prediction of the outbreak timing of marine organisms in the target sea area. According to the nutrient and marine organism-related data of the target sea area, simulate the flux change of a single nutrient element among the state variables of organic mass substances, and then further analyze using the environmental state variables to obtain a support model of nutrients for the entire food web based on time series as the nutrient support model of the target sea area; Based on the allometric measurement theory of ecology, calculate the physiological loss caused by the allometric relationship based on the metabolic theory through the change of individual-level data of marine organisms, and the ecological efficiency function of the loss and gain caused by predation, so as to obtain the energy proportion used for production by marine organisms; By describing the energy flux between species or trophic groups and using the food web method to link community and ecosystem functions, based on the matrix of energy flow between species, simulate the future direction of energy flow in the target sea area to construct an energy flow model; According to the research results of C and N stable isotopes of each producer and consumer species in the target sea area, establish a Bayesian-based stable isotope mixing model to determine the trophic levels of species in the target sea area and their predation and trophic relationships, so as to construct dynamic equations of producers and consumer species in the ecosystem of the target sea area, and thus generate a food web model; Combine the nutrient support model, energy flow model, and food web model to obtain a biological outbreak prediction model to output the prediction results of specified organisms.

[0044] Classify the marine organisms in the target sea area. The classification influencing factors include the biological characteristics of the organisms and the potential clogging severity. The biological characteristics of the organisms involve environmental adaptability, reproduction methods and capabilities, natural enemy distribution, and habitat occupation capabilities. The potential clogging severity involves individual size, the possibility of biological aggregation, floating ability, and the number of individuals. Use a multi-level fuzzy comprehensive evaluation model and conduct qualitative grading and assignment to form a comprehensive risk assessment model to determine the disaster-causing organisms that have an impact on the intake pipeline of the nuclear power plant.

[0045] The following is illustrated with a specific example.

[0046] (1) First, define the investigation scope, as well as the investigation time and frequency.

[0047] 1) Time: 3 years.

[0048] 2) Continuous investigation frequency: Conduct continuously within three years.

[0049] 3) Large-scale investigation frequency: Once a month in the first year; once every two months in the second and third years.

[0050] 4) Shipboard survey frequency: Twice every quarter within three years (for two consecutive spring and neap tide periods).

[0051] 5) The time arrangement is shown in Table 1 and Table 2. The specific investigation dates are determined according to meteorological conditions and sea conditions for safety.

[0052] Table 1 Large-scale investigation operation time arrangement (water chemistry, biology)

[0053]

[0054] Table 2: Shipboard survey operation time arrangement (hydrology, resource quantity)

[0055]

[0056] (2) Contents of hydrological and water quality monitoring

[0057] 1) Continuous observation of the tidal level and flow rate at the water intake

[0058] Set up an observation station outside the open channel water intake of a certain island to conduct long-term observations (for three years) on the tidal level and flow rate. Among them, the tidal level is observed using a bottom-mounted tide gauge, and the flow velocity and direction are observed using a lateral ADCP. The flow rate can be calculated in combination with the topography of the water intake.

[0059] 2) Large-scale station observation of conventional hydrological and meteorological elements

[0060] Investigation elements: items such as air temperature, air pressure, sea surface wind (wind speed and direction), water depth, water temperature, salinity, ocean current (flow velocity and direction), transparency, and water color. The investigation is carried out in accordance with the provisions of Parts 2 and 3 of the Marine Investigation Specification (GBT_12763-2007). The specific observation process is shown in Table 3.

[0061] Table 3 Implementation Sequence and Methods of Hydrometeorological Investigation

[0062] sequence element method Normative references 1 Air temperature Mechanical ventilation psychrometer method GB / T 12763.3-2007 2 Atmospheric pressure Aneroid barometer method GB / T 12763.3-2007 3 Sea surface wind Three-cup anemometer method GB / T 12763.3-2007 3 In-situ water depth Echo sounder method GB / T 12763.2-2007 4 Water temperature Conductivity-temperature-depth (CTD) method GB / T 12763.2-2007 5 Salinity Conductivity-temperature-depth (CTD) method GB / T 12763.2-2007 6 Sea current Direct-reading current meter method GB / T 12763.2-2007 7 Transparency Secchi disk method GB / T 12763.2-2007 8 Water color Water colorimeter method GB / T 12763.2-2007

[0063] 3) Moving observation with an Acoustic Doppler Current Profiler (ADCP)

[0064] During each investigation, the shipborne ADCP conducts moving observations throughout the tidal cycle, traveling back and forth between 8 sections in the large-scale investigation area, with each duration lasting 25 hours. Through stratified processing and harmonic analysis of the measured data, the following indicators are completed:

[0065] Stratified and mean flow velocity and direction at the normal points of each vertical line;

[0066] Mean and maximum flood and ebb flow velocity and direction at each vertical line;

[0067] Mean and maximum flood and ebb flow velocity and direction at each vertical line by stratification;

[0068] Flow velocity and direction process curves and flow velocity vector diagrams at each vertical line (stratified and mean);

[0069] Short-term quasi-harmonic analysis data table, tidal ellipse element table, and tidal ellipse element diagram;

[0070] Flood and ebb tide duration, mean duration, and mean period tables at each vertical line.

[0071] 4) Water chemistry detection

[0072] Conventional water chemistry elements are all observed in a large-scale manner.

[0073] Investigation elements: pH, dissolved oxygen (DO), chemical oxygen demand (COD), BOD5, nutrients (NO3-N, NO2-N, NH4-N, PO4-P, SiO3-Si), petroleum substances, total suspended solids, and heavy metal elements (total mercury, copper, lead, zinc, cadmium, arsenic, chromium). The investigation is carried out in accordance with the provisions of the Marine Monitoring Specification (GB 17378—2007) and the Marine Investigation Specification (GB / T 12763—2007). The specific monitoring indicators and analysis methods are shown in Table 4.

[0074] Table 4 Water Chemistry Monitoring Indicators and Analysis Methods

[0075]

[0076] Sampling instruments: plexiglass water sampler, reversing water sampler.

[0077] Sampling process: After the water sample is taken onto the ship's deck, first fill out the water sample registration form and check the bottle number. Then, immediately sub-sample the water in the following order: dissolved oxygen, pH, five nutrients, etc. For the petroleum sample, use a single-layer water sampler to fix the sample bottle and directly fill it in the water body. Immediately lift it out of the water surface after sampling and extract it on-site.

[0078] (3) Marine biological resources investigation and evaluation content

[0079] 1) Routine biological elements large-scale station observation

[0080] Investigation elements: fecal coliforms, chlorophyll a, phytoplankton, zooplankton, benthic organisms, fish eggs and larvae, nekton, etc. The biological sampling and analysis methods are carried out in accordance with the "Marine Monitoring Specifications" (GB 17378—2007) and the "Marine Survey Specifications" (GB / T 12763—2007), as shown in Table 5.

[0081] Table 5. Biological monitoring indicators and analysis methods

[0082]

[0083]

[0084] Investigation tools:

[0085] Shallow water type I plankton net: Used for collecting large zooplankton, fish eggs and larvae, etc. The inner diameter of the net mouth is 50 cm, and the net mouth area is 0.20 m 2 , and the filtering part is CQ14 or JP12 screen silk, with a total length of 145 cm.

[0086] Shallow water type II plankton net: Used for collecting medium and small zooplankton. The inner diameter of the net mouth is 31.6 cm, and the net mouth area is 0.08 m 2 , and the filtering part is CB36 or JP35 screen silk, with a total length of 140 cm.

[0087] Shallow water type III plankton net: Used for collecting phytoplankton samples for species composition analysis. The inner diameter of the net mouth is 37 cm, and the net mouth area is 0.1 m 2 , and the filtering part is JF62 or JP80 screen silk, with a total length of 140 cm.

[0088] Benthic organism collection: Use a 0.05 m 2 mud sampler to take samples 3 times each time, and the mud sampling area at each station is not less than 0.2 m 2 . And collect all the organisms after washing the mud sample, including biological residues, back to the laboratory for analysis.

[0089] 2) Acoustic resource quantity underway observation

[0090] Investigation instrument: Dual-frequency scientific fishing sonar.

[0091] Investigation method: During the underway survey, a scientific fishing sonar is mounted on the side of the ship for the investigation of biological resources. The fixed installation is carried out by side mounting, aiming to obtain the spatial distribution characteristics of biological resources in the entire large-area observation sea area.

[0092] Types of biological resources investigated: Evaluate the resource quantity and spatial distribution of marine organisms in the investigated sea area, with a focus on three major types of cold-source disaster-causing organisms in nuclear power plants, namely fish, jellyfish, and mysid shrimp.

[0093] Biological resource quantity assessment process: a. Calibration of acoustic instruments; b. Collection of biological data, sampling with fishing gears such as gillnets, throw nets, fishing tackle, or pots with relatively low selectivity during the acoustic survey; c. Data processing; d. Processing of acoustic echo image data; e. Target strength of fish; f. Resource density; g. Assessment of resource quantity.

[0094] Completion indicators: Calibration results of the sonar system, navigation noise spectral level map of the investigation vessel, horizontal distribution map of biological density, frequency distribution map and water layer distribution map of single targets, target strength spectral map of typical cold-source organisms, etc.

[0095] 3) Improvement and optimization of the cold-source risk identification calendar

[0096] According to the monthly investigation results of marine organisms in the waters around Taishan Nuclear Power Plant, evaluate the monthly and seasonal biomass and succession laws of marine organisms; combined with the historical data collected, evaluate the interannual change laws of marine organisms in the risk calendar; through the biological habits of typical marine organisms and the correlation analysis with the environment, explore the biomass change and species succession laws of cold-source organisms in this sea area, improve and optimize the cold-source risk identification calendar, and achieve accurate prediction of the outbreak of typical marine organisms in this sea area. The effect diagram of the risk identification calendar is shown in Table 6. In Table 6, blank indicates no risk, low indicates general risk, medium indicates medium risk, and high indicates high risk.

[0097] Table 6 Statistical table of the time series of cold-source risk marine organisms

[0098]

[0099] (4) Content of developing a prediction model

[0100] 1) Data acquisition and integration

[0101] The main data used are the multi-sensor data (continuous real-time sampling) collected from a nuclear power plant through conventional surveys and online monitoring. Among them, the conventional survey data are mainly the hydrological, water quality, and biological data obtained after ship-based cruising observations and sampling surveys. The online monitoring data are mainly the data on marine biological density, ocean current velocity, the angle between the ocean current and the water intake, sea breeze, the angle between the sea breeze and the water intake, seawater temperature, and seawater salinity obtained by using buoys equipped with image sonars, temperature sensors, and flow sensors, etc.

[0102] During the monitoring of marine organisms at the water intake of the nuclear power plant, detectors with different functions are used. These entities are distributed according to the structure of the power plant's water intake, collect data separately, and process them. There are significant differences in the data types and their elements of different detectors. In the design of the real-time online monitoring and early warning system and the monitoring of marine organisms at the water intake of the nuclear power plant, if the ontology model of multi-source heterogeneous data is established by fusing at the raw data level, the amount of data will be too large, which will inevitably affect the calculation speed and efficiency of the system. Therefore, a preprocessing method must be adopted to screen the key elements of each entity and construct local entity databases separately, which are used as the input for data reconstruction and further data integration.

[0103] 2) Large-scale ROMS hydrodynamic model

[0104] Based on the ROMS model, a three-dimensional hydrodynamic model for a large marine area is developed. Horizontally, curvilinear orthogonal latitude and longitude coordinates are used, and vertically, terrain-following S coordinates are used. The model considers atmospheric forcing (momentum flux, short-wave and long-wave radiation fluxes, sensible heat and latent heat fluxes, evaporation and precipitation freshwater fluxes, etc.), open boundary forcing in the open sea (currents and tide levels), and river input (discharge). According to the requirements, the model simulation and forecast data are output, including sea currents, water temperature, salinity, and water level, and open boundary driving data are provided for the high-resolution SCHISM or FVCOM hydrodynamic model in the Taishan offshore area. The specific R & D process is as follows:

[0105] a Plan the calculation area of the South China Sea ROMS model and design the model calculation grid;

[0106] b Build the ROMS model on the model grid in the South China Sea area to enable it to operate stably initially;

[0107] c Design the input and output interfaces and input and output data formats of the ROMS model;

[0108] d Debug and improve the operation efficiency of the ROMS model to meet the project requirements;

[0109] e Calibrate various parameters of the ROMS model to make the simulation results of the ROMS model basically consistent with the actual situation.

[0110] 3) High-resolution SCHISM or FVCOM hydrodynamic model

[0111] Develop a high-resolution three-dimensional hydrodynamic model for the coastal waters of Taishan based on the SCHISM or FVCOM model. Unstructured triangular or quadrilateral grids are used horizontally to fit the shoreline and terrain changes, and terrain-following S coordinates or SZ hybrid coordinates are used vertically. The model takes into account atmospheric forcing (momentum flux, short-wave and long-wave radiation flux, sensible heat and latent heat flux, evaporation and precipitation freshwater flux, etc.), open boundary forcing in the open sea (current and tidal level), river input (flow rate), as well as the intake and discharge of nuclear power. Output model simulation and prediction data as required, including sea current, water temperature, salinity and water level, and provide flow field information for the particle tracking model. The specific R & D process is as follows:

[0112] a Plan the calculation area of the high-resolution hydrodynamic model for the coastal waters and design the model calculation grid;

[0113] b Build a high-resolution hydrodynamic model on the model grid in the coastal area to enable it to operate stably initially;

[0114] c Design the input / output interface and input / output data format of the high-resolution hydrodynamic model;

[0115] d Debug and improve the high-resolution hydrodynamic model to improve the operation efficiency and make it meet the project requirements;

[0116] e Calibrate various parameters of the high-resolution dynamic model to make the model simulation results basically consistent with the actual situation.

[0117] 4) Biological drift numerical model

[0118] Interpolate the observed information such as biomass and outbreak location into the ocean model at different times as the initial conditions and verification information of the Lagrangian model. Combine the sea surface current field simulated by the ROMS ocean model and the Stokes drift calculated by the wave model, and use the Lagrangian model to track the forward and backward trajectories of organisms. The technical route is shown in the figure. Based on the accurate three-dimensional temperature, salinity and current simulation and prediction, analyze the swimming and drifting characteristics of typical marine organisms, and establish the migration trajectory model of these organisms. This model not only requires accurate flow field information, but also needs to integrate the biological habits and swimming characteristics of typical organisms, such as periodic water layer distribution, swimming tendency and speed, etc. Therefore, establish a three-dimensional trajectory tracking and prediction model; through assimilating the real-time monitoring results, study the trajectory backtracking and destination distribution probability of marine organisms, and realize the prediction of the migration path of marine organisms; develop a marine organism prediction system to forecast and display the movement trajectory of marine organisms and calculate the possible time for marine organisms to enter the harbor basin. Take the real-time monitored location of marine organisms as the initial point and correction point, continuously correct the trajectory parameters, update the calculation results, and finally realize the simulation and prediction of the migration path of marine organisms.

[0119] 5) Biological outbreak prediction model

[0120] From the three aspects of matter, energy, and biological interactions, a nutrient support model, an energy flow model, and a food web model based on the ecosystem of Taishan cold source sea area are established respectively.

[0121] a Using the nutrient and biological data obtained from the investigation and indoor research parts, simulate the flux changes of a single nutrient element among the state variables of organic mass. Then, further analyze the environmental state variables into two depth layers in the water column and a seabed sediment layer to obtain a nutrient support model for the entire food web based on time series.

[0122] b Based on the allometric measurement theory of ecology, calculate the physiological losses brought by the allometric relationship based on the metabolic theory, and the ecological efficiency function of losses and gains caused by predation through the changes in individual-level data of marine organisms, so as to describe the proportion of energy used for the production of marine organisms and construct an energy flow model. By describing the energy flux between species or trophic groups, link the community composition with the ecosystem function through the food web method. Based on the matrix of energy flow between species, simulate the future direction of energy flow in this sea area to predict the outbreak of a specific ethnic group (harmful algae or planktonic / nektonic animals).

[0123] c According to the C and N stable isotope research results of various producers and consumers in the project, establish a Bayesian-based stable isotope mixing model to determine the trophic levels of the main species in the ecosystem of the nuclear power cold source sea area and their predation and trophic relationships. Based on the above predation and trophic relationship model, construct the dynamic equations of producers and consumers in the ecosystem of this sea area and generate a high-resolution three-dimensional food web model.

[0124] d Finally, based on the determination of the multivariate correlation of "environmental factors - phytoplankton - disaster-causing organisms" in the prediction results of the nutrient support model, energy flow model, and food web model, use AI technology to intervene in biological prediction and output the intelligent prediction results of disaster-causing organisms.

[0125] 6) Comprehensive risk assessment model

[0126] Disaster-causing organisms refer to marine organisms that may cause blockage of the water intake pipeline of the nuclear power cold source and affect the safe operation of nuclear power units. Generally speaking, they refer to organisms with an individual or group diameter larger than the mesh diameter of the filter screen. According to the actual interception net situation of nuclear power, screen according to the biological characteristics of organisms and the potential blockage severity. Among the biological characteristics, mainly consider environmental adaptability, reproduction methods and capabilities, natural enemy distribution, and habitat occupation capabilities. The blockage severity index mainly considers individual size, biological aggregation possibility, floating property, and individual quantity. Referring to the method of risk assessment, use a multi-level fuzzy comprehensive evaluation model and conduct qualitative grading and assignment. The calculation formula is as follows:

[0127] D = C × B

[0128] Where D is the disaster-causing risk index, C is the biological characteristic index, and B is the blockage severity index.

[0129]

[0130] Where i is the index factor, and Ci is the graded membership assignment of the biological characteristic index factor i.

[0131]

[0132] Where, b i is the graded membership assignment of the hazard possibility index factor i, and W Bi is the weight of the hazard index factor i. The aggregation intensity characteristic is measured by the species occurrence frequency and the clumping index.

[0133] The calculation formula of the clumping index I is as follows: Where S 2 represents the variance of the sample, represents the mean of the sample. When I is greater than 0, it indicates that the zooplankton has the characteristic of aggregated distribution. The higher the positive value, the higher the aggregation intensity it can reflect.

[0134] The weight of the blockage severity index factor is determined by the expert ranking method, and the weight distribution calculation formula is:

[0135] B i = 2[m(1 + n) - R i / [mn(1 + n)]

[0136] Where B is the weight of the i-th factor, m is the number of experts, n is the number of factors, and R i is the rank sum of the i-th (i = 1, 2,..., n) factor.

[0137] The classification criteria for the disaster-causing possibility D are as follows: 0 - 0.249 low risk; 0.25 - 0.499 medium risk, 0.50 - 0.749 high risk, 0.750 - 1.000 extremely high risk.

[0138] 7) Model verification

[0139] The water level, temperature, salinity data at the initial moment of the model adopt measured data, including the water quality monitoring data of the water quality section, the initial hydrological and water quality data obtained by interpolation, various data from previous investigations, basic satellite remote sensing data over a large area, and meteorological monitoring and forecasting data. The model verification is mainly carried out for hydrodynamic, temperature, salinity, and water quality respectively. A total of 7 model verification points are designed, covering the project research area. The model verification is completed by comparing the actual observed data of the project marine water quality and hydrological element investigation stations, the harmonic constants of the existing tide gauges in the calculation area, and the time series water level, sea current, harmonic constants, temperature, salinity, and water quality data simulated by the numerical model.

[0140] (5) Development of early warning platform

[0141] This platform is to prevent potential safety hazards at the nuclear power cold source water intake, and construct an integrated and comprehensive nuclear power water intake disaster comprehensive management information platform integrating seawater online monitoring, prediction and forecasting, risk analysis, and emergency response, so as to realize the integration and fusion application of comprehensive information, personnel, and equipment data of nuclear power plants. See Figure 2 and Figure 3 , which consists of sonar, camera, tensiometer, meteorograph, and other auxiliary functions, etc., and can be customized according to needs, presenting all devices vividly and completely in the system, and the network architecture.

[0142] Two access methods are adopted, namely the Web end and the APP end.

[0143] a. After logging in to the Web end, enter the home page to see the overall situation of the entire platform. By clicking on different menus in the navigation bar, enter different sub-pages, including the net, sonar, statistics, and records, etc. The management and service platform mainly includes three parts. The left side is the display area of marine real-time observation and historical investigation statistical data, the middle part is the display area of numerical simulation hourly output and the predicted path of disaster-causing objects, and the right side is the display area of the decision-making assistance support system.

[0144] b. After logging in to the APP end, enter the home page. The APP data is synchronized with the Web end. By clicking on different menus in the navigation bar, enter different sub-pages, including monitoring, workbench, etc.

[0145] The marine organism prediction method provided by the present invention for the waters around nuclear power plants analyzes and studies the outbreak time and spatial distribution of marine organisms adjacent to nuclear power plants, the relationship between the dynamic field in the surrounding waters and the drift of disaster-causing organisms with the ocean current, and the connection between the flow rate change at the water intake and the flux of disaster-causing organisms entering the open channel; uses a fixed spatial sampling method for the hydrological, hydrochemical factors, nutrients, and marine plankton in the research waters, and conducts investigations regularly; at the same time, adds acoustic technical means to conduct an overall assessment of marine biological resources and grasp the proportional change law of the biological resource amounts at all levels of the entire ecosystem in different seasons; by establishing a large-region ocean dynamic model and a disaster-causing object drift model, and developing application services, early warning models, and software platforms for the client, the accurate trajectory prediction and outbreak time prediction of marine organisms are realized.

[0146] The above are only the preferred embodiments of the present invention, and do not limit its patent scope accordingly. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present invention, directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for predicting marine organisms in the sea area around a nuclear power plant, characterized in that, It includes the following steps: First, determine the survey scope, time, and frequency of the target sea area, and conduct the survey by combining continuous observation, large-area observation, and underway observation; S1. Conduct hydrological and water quality monitoring and marine biological resource investigation and evaluation on the target sea area. Among them, the hydrological and water quality monitoring includes continuous observation of the tide level and flow rate at the intake of the nuclear power plant, large-area observation based on hydrometeorological elements, large-area observation based on water chemical elements, and underway observation with an acoustic Doppler current profiler; S2. Establish a three-dimensional hydrodynamic model of the target sea area based on the monitoring data of hydrology and water quality; S3. Interpolate the marine biomass and corresponding outbreak location information obtained from the survey into different moments of the three-dimensional hydrodynamic model to establish a biological drift numerical model. Use the biological drift numerical model to simulate and predict the migration path of marine organisms, including: by assimilating the results of real-time monitoring surveys, calculating and correcting the trajectory backtracking and destination distribution probability of marine organisms, and using the real-time monitored locations of marine organisms as the initial points and correction points to continuously correct the trajectory parameters to achieve the prediction optimization of the migration path of marine organisms; According to the data related to nutrients and marine organisms in the target sea area, simulate the flux changes of a single nutrient element between the state variables of organic mass, and then further analyze using environmental state variables to obtain a support model of nutrients for the entire food web based on time series, as the nutrient support model of the target sea area; Based on the allometric measurement theory of ecology, calculate the physiological loss caused by the allometric relationship based on the metabolic theory and the ecological efficiency function of the loss and gain caused by predation through the changes in individual-level data of marine organisms, so as to obtain the energy proportion of marine organisms used for production; By describing the energy flux between species or trophic groups and using the food web method to link community and ecosystem functions, based on the matrix of energy flow between species, simulate the future direction of energy flow in the target sea area to construct an energy flow model; According to the research results of C and N stable isotopes of each producer and consumer species in the target sea area, establish a Bayesian-based stable isotope mixing model to determine the trophic levels of species in the target sea area and their predation and trophic relationships, so as to construct the dynamic equations of producer and consumer species in the ecosystem of the target sea area, thereby generating a food web model; Combine the nutrient support model, energy flow model, and food web model to obtain a biological outbreak prediction model to output the prediction results of specified organisms; Classify the marine organisms in the target sea area. The classification influencing factors include the biological characteristics of the organisms and the potential blockage severity. The biological characteristics of the organisms involve environmental adaptability, reproduction methods and capabilities, natural enemy distribution, and the ability to occupy habitats. The potential blockage severity involves individual size, the possibility of biological aggregation, floatability, and the number of individuals. Use a multi-level fuzzy comprehensive evaluation model and perform qualitative grading assignments to form a comprehensive risk assessment model to determine the disaster-causing organisms that have an impact on the intake pipeline of the nuclear power plant. The comprehensive risk assessment model uses the following calculation formula for judgment: D = C × B, where D is the disaster-causing risk index, C is the biological characteristics index, and B is the blockage severity index; where i is the index factor, and C i is the grading membership assignment of the biological characteristics index factor i; where bi is the grading membership assignment of the possibility index factor i, and W Bi is the weight of the blockage severity index factor i, which is determined by the expert ranking method. The weight distribution calculation formula is: W Bi = 2[m(1 + n) - R i / [mn(1 + n)], where W Bi is the weight of the i-th factor, m is the number of experts, n is the number of factors, and R i is the rank sum of the i-th factor. The comprehensive risk assessment model classifies marine organisms into different hazard levels according to different disaster-causing risk indexes; Based on the marine biological resource survey data, evaluate the monthly / quarterly biomass and succession law of marine organisms, combine with the historical data of the target sea area, evaluate the interannual change law of marine organisms in the risk calendar, and through the analysis of the biological habits of marine organisms in the target sea area and their correlation with the environment, obtain the biomass change and species succession law of marine organisms in the target sea area, optimize the cold source risk identification calendar to achieve the prediction of the outbreak timing of marine organisms in the target sea area; Using the output results of the biological outbreak prediction model and the comprehensive risk assessment model, obtain the prediction of the risk level of marine organisms in the target sea area for the intake water of the nuclear power plant, and issue corresponding early warning prompts.

2. The method for predicting marine organisms in the sea area around a nuclear power plant according to claim 1, characterized in that In step S2, the three-dimensional hydrodynamic model adopts ROMS, SCHISM or FVCOM.

Images