Cold source external risk early warning and forecasting system

Abstract

The invention provides a cold source external risk early warning and forecasting system. The cold source external risk early warning and forecasting system comprises a map calling module, a hydrodynamic model, a pollution source forecasting module, a red tide monitoring and forecasting module, a marine litter monitoring module, a marine organism density monitoring module and a drift trajectory forecasting module. The map calling module is used for calling satellite map data and constructing a monitoring area map; the hydrodynamic model is used for calling sea chart data and tide level data of a monitored area, driving a water body to move at a model open boundary based on the sea chart data by using the tide level data, calculating flow field data by using an FVCOM model, and constructing a flow field distribution diagram; the tide level data can be obtained from a public database TPXO global tide model; and other modules carry out corresponding monitoring based on the flow field distribution diagram. According to the invention, the sources of pollutants and cold source blockages and the moving trend of the pollutants and cold source blockages on the sea in the future can be rapidly and conveniently obtained, so that related units can rapidly make early warning and forecasting work.

Description

Technical Field

[0001] The present invention relates to the technical field of safety monitoring and early warning for the cold source of power plants, and particularly relates to an early warning and forecasting system for external risks of the cold source. Background Art

[0002] The operating status of the cold source water intake system of a coastal nuclear power plant is directly related to the safety and reliability of the nuclear power plant. In recent years, with the changes in the marine ecological environment, the abnormal outbreak of marine organisms or the massive aggregation of foreign objects blocking the water intake system have brought adverse effects to the normal operation of the cold source system of coastal nuclear power plants, and even led to events such as unit power reduction, tripping, and even emergency shutdown, directly affecting the economy, reliability, and safety of nuclear power plants.

[0003] WANO (World Association of Nuclear Operators) analyzed 44 major events related to the blockage of cold source water intake ports that occurred from 2004 to 2007 in the "Report on Important Operating Events" (WANO SOER 2007-2). Among the substances that caused the blockage events, 84% were aquatic organisms, 9% were ice formation, 5% were sand and silt, and 2% were crude oil. Since 2000, there have been more than 230 power plant outage events caused by organisms (foreign objects) resulting in the failure of the cold source system in the United States, the United Kingdom, Sweden, Israel, South Africa, Australia, Japan, South Korea, and China, etc., causing major potential safety hazards and huge economic losses. The substances causing the blockage of the water intake system are divided into two categories: biological and non-biological. Biological substances mainly include phytoplankton, seagrass, water hyacinth, jellyfish, sea cucumbers, razor clams, ulva lactuca, jellyfish, shrimp, fish, etc., among which jellyfish-caused blockage events are the most common; non-biological substances mainly include ice, garbage, sediment, wheat straw, etc. In recent years, several events have occurred in nuclear power plants in China where the cold source water intake system was blocked due to the aggregation or explosive growth of marine organisms, thus affecting the water intake safety.

[0004] On the one hand, the warm seawater caused by the nuclear power plant's temperature discharge may have a certain impact on the marine organisms and even the marine ecosystem in the sea area where it is located; on the other hand, ecological disasters such as red tides and outbreaks of marine organisms caused by eutrophication and global change may affect the water used by the cold source system.

[0005] Due to the constraints of human, material, time, and space conditions in actual on-site observations, it is not possible to conveniently and quickly obtain the sources and specific distribution of relevant pollutants, which affects the relevant early warning and forecasting work. In order to further trace the sources of pollutants and blockages and their future impact trends, it is necessary to construct a more convenient and effective early warning and forecasting system for external risks of the cold source. Summary of the Invention

[0006] The present invention aims to provide an early warning and forecasting system for external risks of a cold source, which can quickly and conveniently obtain the sources of pollutants and cold source blockages and their future movement trends at sea, so that relevant units can quickly carry out early warning and forecasting work.

[0007] The technical solution of the present invention is as follows:

[0008] The early warning and forecasting system for external risks of a cold source includes a map calling module, a hydrodynamic model module, a pollution source prediction module, a red tide monitoring module, a marine biological density monitoring module, and a drift trajectory prediction module;

[0009] The map calling module is used to call satellite map data and construct a map of the monitoring area;

[0010] The hydrodynamic model module is used to call the chart data and tide level data of the monitoring area, drive the water body movement with the tide level data at the open boundary of the model based on the chart data, and use the FVCOM model to calculate the flow field data and construct a flow field distribution map; the open boundary tide level data can be obtained from the public database TPXO global tide model;

[0011] The red tide monitoring module is based on satellite real-time image data and red tide algae regular monitoring data, and can timely detect red tides occurring in the area. When the monitoring data exceeds the red tide outbreak threshold and may threaten the cold source, an alarm is issued;

[0012] The marine litter monitoring module is based on offshore fixed-point and regular monitoring and coastal fixed-point and regular monitoring data. When the amount of marine litter at the monitoring point exceeds the standard and may threaten the cold source, an alarm is issued;

[0013] The pollution source prediction module is based on a given pollution source input in advance, calls the flow field data calculated by the hydrodynamic model, and uses the DYE staining module in the FVCOM model to calculate the diffusion range and diffusion concentration of pollutants after being released from the source; when the predicted concentration of pollutants at the preset position exceeds the threshold, an alarm is issued;

[0014] The marine biological density monitoring module compares each monitoring data of the marine biological density with the preset parameters by inputting the monitoring data of multiple monitoring points set in the monitoring area. When the types and quantities of biological resources are abnormal, an alarm is issued;

[0015] The drift trajectory prediction module is used to simulate the movement trajectory of the cold source blockage. By calling the flow field data calculated by the hydrodynamic model and based on the Lagrangian particle tracking model in the FVCOM model, it predicts the future movement trajectory of the cold source blockage or retraces its movement route to trace the source, and proposes targeted measures according to the preset specifications.

[0016] The red tide monitoring and prediction module described above includes a red tide algae monitoring sub-module, a buoy monitoring sub-module, and a buoy alarm sub-module;

[0017] The red tide algae monitoring sub-module detects red tide algae data based on ocean satellite monitoring and daily manual sampling, and inputs the data into the red tide algae monitoring sub-module. If the ocean satellite data shows that a red tide may occur in the monitored area, an alarm is issued; if the red tide algae data detected by manual sampling exceeds the preset risk level threshold parameter, an alarm corresponding to the risk level is issued;

[0018] The buoy monitoring sub-module obtains the seawater biochemical indexes detected by the buoy sensors from each buoy set in the monitored area through wireless communication, and compares them with the preset parameter thresholds. If the set parameter thresholds are exceeded, a risk level alarm is issued through the buoy alarm sub-module.

[0019] The marine organism density monitoring module includes a zooplankton monitoring sub-module, a phytoplankton monitoring sub-module, a benthic organism monitoring sub-module, a stow net method monitoring sub-module, and a drift net method monitoring sub-module;

[0020] The zooplankton monitoring sub-module is used to input the sample monitoring data of each manual sampling point in the monitored area, record the zooplankton density, and compare it with the preset risk level threshold parameter. When it is higher than the preset parameter, an alarm corresponding to the risk level is issued;

[0021] The phytoplankton monitoring sub-module is used to input the sample monitoring data of each manual sampling point in the monitored area, record the phytoplankton density, and compare it with the preset risk level threshold parameter. When it is higher than the preset parameter, an alarm corresponding to the risk level is issued;

[0022] The benthic organism monitoring sub-module is used to input the sample monitoring data of each manual sampling point in the monitored area, record the benthic organism density, and compare it with the preset risk level threshold parameter. When it is higher than the preset parameter, an alarm corresponding to the risk level is issued;

[0023] The stow net method monitoring sub-module is used to set stow nets in the monitored area, monitor the types and quantities of biological resources caught by the stow nets and the marine garbage caught through wireless communication and manual sampling, input the monitoring data into the stow net method monitoring sub-module, and compare it with the preset risk level threshold parameter. When it is higher than the preset parameter, an alarm corresponding to the risk level is issued;

[0024] The drift net method monitoring module is used to set drift nets within the monitoring area. By means of wireless communication and manual sampling, it monitors the types and quantities of biological resources caught by the drift nets, and simultaneously monitors the marine garbage caught. The monitoring data is input into the drift net method monitoring sub-module and compared with the preset risk level threshold parameters. When it is higher than the preset parameters, an alarm corresponding to the risk level is issued.

[0025] The marine biological density monitoring module further includes a trawl method monitoring sub-module and a shipborne sonar monitoring sub-module; the trawl method monitoring sub-module is used to record and monitor the types and quantity data of biological resources obtained by trawl fishing when the patrol ship's patrol monitoring is abnormal. By comparing with the preset parameters, when an abnormality occurs, a corresponding alarm is issued;

[0026] The shipborne sonar monitoring sub-module is used to obtain the types and density data of biological resources monitored by the patrol ship through wireless communication, and issue an alarm when the types and quantities of biological resources are abnormal.

[0027] The cold source external risk early warning and forecasting system further includes an offshore alarm module. The offshore alarm module is connected to each module and issues an alarm according to the data abnormality instructions sent by each module.

[0028] The cold source external risk early warning and forecasting system further includes a disaster-causing substance monitoring daily report module. The disaster-causing substance monitoring daily report module is used to record the daily monitoring data of the red tide monitoring module, the pollution source prediction module, and the marine biological density monitoring module, and analyze and judge the abnormalities of the data to generate an analysis report.

[0029] The cold source external risk early warning and forecasting system further includes a user management module and a role management module;

[0030] The user management module is used for user registration and assigns user query permissions;

[0031] The role management module is used to assign user editing permissions.

[0032] The specific process of predicting the future movement trajectory of the cold source blockage or tracing back its movement route based on the Lagrangian particle tracking model in the FVCOM model is as follows:

[0033] In a three-dimensional (x, y, z) space, particles can be traced by solving the x, y, and z velocity equations.

[0034]

[0035] Suppose is the position of the particle at t = t n moment, then the particle is at t = t n+1 (i.e., tn Position at time (+Δt) In three-dimensional space, it can be determined by the following fourth-order Runge-Kutta (ERK) algorithm:

[0036]

[0037]

[0038] where u, v, are the velocity components of x, y, and σ respectively;

[0039] Based on the above fourth-order ERK algorithm:

[0040] When predicting the future movement trajectory of the cold source blockage, input the initial position of the blockage, i.e., the particle, the flow field data calculated according to the FVCOM model, and the time step. Through the following formula:

[0041] Position at the next time point = current position + flow velocity at the current time point × time step (8)

[0042] Calculate the position of each particle at the next time step, and then by analogy, calculate the positions of each particle at multiple future time steps, so as to predict the future movement trajectory of the blockage;

[0043] When tracing back the previous movement trajectory of the blockage, input the position of the blockage at the current time point, the reverse flow velocity at the previous time point, and the time step. Through the following formula:

[0044] Position at the previous time point = current position - reverse flow velocity at the previous time point × time step (9)

[0045] Calculate the position of each particle at the previous time step, and then by analogy, calculate the positions of each particle at multiple past time steps, so as to trace back the source of the cold source blockage.

[0046] The cold source blockage mentioned above includes red tides, marine garbage, other marine organisms, etc.

[0047] The beneficial effects of the present invention are as follows:

[0048] The external risk early warning and forecasting system of the cold source of the present invention predicts through a hydrodynamic model, comprehensively judges the future influence trends (or traceability) of dangerous factors such as red tides, oil spills, and marine floating objects on marine sensitive targets, gives early warning information, and can be used to forecast the monitoring, tracking and tracing, and early warning of tides, red tides, and other aquatic organisms; this system can input real-time simulation calculations, visualize the model results, and has an interactive function.

[0049] The cold source external risk early warning and forecasting system of the present invention can quickly and conveniently obtain the sources of pollutants and cold source blockages and their future movement trends at sea, so that relevant units can quickly carry out early warning and forecasting work, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the FVCOM triangular grid structure; DETAILED DESCRIPTION OF THE INVENTION

[0051] The present invention will be specifically described below in conjunction with embodiments.

[0052] Embodiment 1

[0053] The cold source external risk early warning and forecasting system includes a map calling module, a hydrodynamic model module, a pollution source prediction module, a red tide monitoring module, a marine biological density monitoring module, a drift trajectory prediction module, an offshore alarm module, a disaster-causing object monitoring daily report module, a user management module, and a role management module;

[0054] The map calling module is used to call satellite map data and construct a monitoring area map;

[0055] The hydrodynamic model module is used to call the chart data and tide level data of the monitoring area, drive the water body movement based on the chart data at the open boundary of the model with the tide level data, use the FVCOM model to calculate the flow field data, and construct a flow field distribution map; the tide level data at the open boundary can be obtained from the public database TPXO global tide model;

[0056] The FVCOM model used in the hydrodynamic model, that is, the finite volume coastal ocean numerical model, mainly uses the σ coordinate in the vertical direction and adopts the turbulence closure model, namely the Mellor-Yamada model, in the vertical mixing calculation. This model combined with the σ coordinate can better simulate the bottom boundary conditions. In addition, FVCOM also adopts the method of separating the internal and external models for calculation. Among them, the external model is two-dimensional and is mainly responsible for the calculation of water level and average flow velocity, and the internal model is three-dimensional and is mainly responsible for calculating three-dimensional flow velocity, temperature, salinity and turbulence coefficient. The method of separating the internal and external models greatly improves the calculation efficiency of the model.

[0057] As shown in Equations (10) to (16), the original control equations of FVCOM mainly consist of the following equations such as the momentum equation: (1) Momentum equation:

[0058]

[0059] (2) Continuity equation:

[0060]

[0061] (3) Temperature equation:

[0062]

[0063] (4) Salinity equation:

[0064]

[0065] (5) Density equation:

[0066] ρ = ρ(T, S) (16)

[0067] where x, y, and z represent east, north, and the vertical axis, respectively, in the Cartesian rectangular coordinate system; u, v, and w are the velocity components in the x, y, and z directions; T is the temperature; S is the salinity; ρ is the density; P is the pressure; f is the Coriolis parameter; g is the acceleration due to gravity; K m is the vertical eddy viscosity coefficient; K h is the vertical eddy diffusion coefficient of heat; F u and F v as well as F T , F S represent the diffusion terms of horizontal momentum, heat, and salinity, respectively.

[0068] When performing numerical simulation calculations, FVCOM can divide the study sea area into irregular and non - overlapping triangular grids. A triangular grid consists of three nodes, a centroid, and three edges. The specific structural schematic diagram is as Figure 1 shown: where the node ● represents the H, ζ, ω, D, s, θ, q 2 , q 2 l, A m , K h The centroid represents the calculated flow velocities u and v, and F represents various net fluxes.

[0069] Assume that N and M represent the total number of triangular grids and grid nodes in the calculation area, respectively. Then the coordinates of the center of the triangular grid and the grid node coordinates can be expressed by the following formulas:

[0070] [X(i), Y(i)] i , i = 1:N (17)

[0071] [X n (j), Y n (j)] j , j = 1:M (18)

[0072] In each triangular grid, its three nodes are represented by and is integrated clockwise from 1 to 3. Adjacent triangles with a common edge can is represented. At the open boundary or shore boundary, is equal to 0. The number of triangular meshes contained at each node is denoted as NT(j) and can be calculated by the integral number NB i (m), where m is integrated from 1 to NT(j) in the clockwise direction.

[0073] When FVCOM performs numerical calculations, it is carried out at different positions of the triangular meshes, where H, ζ, ω, D, s, θ, q 2 ,q 2 l, A m ,K h are calculated at the grid nodes, while u and v are calculated at the center of the triangular meshes. In the vertical direction, all model variables are located in the middle of each σ layer, except for variables such as ω, q 2 ,q 2 l, etc.;

[0074] The described red tide monitoring module is based on satellite real-time image data and red tide algae regular monitoring data to promptly detect red tides occurring in the region. When the monitoring data exceeds the red tide outbreak threshold and may threaten the cold source, an alarm is issued;

[0075] The described red tide monitoring and prediction module includes an algae monitoring sub-module, a buoy monitoring sub-module, and a buoy alarm sub-module;

[0076] The described red tide algae monitoring sub-module inputs the red tide algae data detected by ocean satellite monitoring and daily manual sampling into the red tide algae monitoring sub-module. If the ocean satellite data shows that a red tide may occur in the monitored area, an alarm is issued; if the red tide algae data detected by manual sampling exceeds the preset risk level threshold parameter, an alarm corresponding to the risk level is issued;

[0077] The described buoy monitoring sub-module obtains the seawater biochemical indicators detected by the buoy sensors through wireless communication from each buoy set in the monitored area and compares them with the preset parameter thresholds. When the set parameter thresholds are exceeded, a risk level alarm is issued through the buoy alarm sub-module.

[0078] The described marine debris monitoring module is based on the data of fixed-point and regular monitoring in the open sea and on the coast. When the amount of marine debris at the monitoring point exceeds the standard and may threaten the cold source, an alarm is issued;

[0079] The described pollution source prediction module is based on the given pollution sources input in advance, calls the flow field data calculated by the hydrodynamic model, and uses the DYE staining module in the FVCOM model for calculation to predict the diffusion range and diffusion concentration of pollutants after being released from the source; when the predicted concentration of pollutants at the preset position exceeds the threshold, an alarm is issued;

[0080] The described marine organism density monitoring module compares each monitoring data of the marine organism density with preset parameters by inputting the monitoring data of multiple monitoring points set in the monitoring area, and issues an alarm when the types and quantities of biological resources are abnormal;

[0081] The described marine organism density monitoring module includes a zooplankton monitoring sub-module, a phytoplankton monitoring sub-module, a benthic organism monitoring sub-module, a stow net method monitoring sub-module, and a drift net method monitoring sub-module;

[0082] The zooplankton monitoring sub-module is used to input the sample monitoring data of each artificial sampling point in the monitoring area, record the zooplankton density, compare it with the preset risk level threshold parameter, and issue an alarm corresponding to the risk level when it is higher than the preset parameter;

[0083] The phytoplankton monitoring sub-module is used to input the sample monitoring data of each artificial sampling point in the monitoring area, record the phytoplankton density, compare it with the preset risk level threshold parameter, and issue an alarm corresponding to the risk level when it is higher than the preset parameter;

[0084] The benthic organism monitoring sub-module is used to input the sample monitoring data of each artificial sampling point in the monitoring area, record the benthic organism density, compare it with the preset risk level threshold parameter, and issue an alarm corresponding to the risk level when it is higher than the preset parameter;

[0085] The stow net method monitoring sub-module is used to set a stow net in the monitoring area, monitor the types and quantities of biological resources caught by the stow net through wireless communication and manual sampling, and at the same time monitor the marine garbage caught, input the monitoring data into the stow net method monitoring sub-module, and compare it with the preset risk level threshold parameter. When it is higher than the preset parameter, an alarm corresponding to the risk level is issued;

[0086] The drift net method monitoring module is used to set a drift net in the monitoring area, monitor the types and quantities of biological resources caught by the drift net through wireless communication and manual sampling, and at the same time monitor the marine garbage caught, input the monitoring data into the drift net method monitoring sub-module, and compare it with the preset risk level threshold parameter. When it is higher than the preset parameter, an alarm corresponding to the risk level is issued.

[0087] The described marine organism density monitoring module also includes a trawl method monitoring sub-module and a shipborne sonar monitoring sub-module; the trawl method monitoring sub-module is used to record and monitor the types and quantity data of biological resources obtained by trawl fishing when the patrol ship patrols and monitors abnormalities. By comparing with the preset parameters, when an abnormality occurs, a corresponding alarm is issued;

[0088] The on-board sonar monitoring sub-module is used to obtain the types and density data of biological resources monitored during the patrol of the patrol boat through wireless communication, and issue an alarm when the types and quantities of biological resources are abnormal.

[0089] The drift trajectory prediction module is used to simulate the movement trajectory of the cold source blockage. Based on the flow field data calculated by the hydrodynamic model and the Lagrangian particle tracking model in the FVCOM model, it predicts the future movement trajectory of the cold source blockage, or retraces its movement route to trace the source, and proposes targeted measures according to the preset specifications. The cold source blockage includes red tides, marine garbage, and other marine organisms.

[0090] The specific process of predicting the future movement trajectory of the cold source blockage based on the Lagrangian particle tracking model in the FVCOM model, or retracing its movement route is as follows:

[0091] In a three-dimensional (x, y, z) space, particles can be tracked by solving the x, y, and z velocity equations.

[0092]

[0093] Suppose is the position of the particle at t = t n moment, then the position of the particle at t = t n+1 (i.e., t n +Δt) moment In three-dimensional space, it can be determined by the following fourth-order Runge-Kutta (ERK) algorithm:

[0094]

[0095]

[0096] Among them, u, v, are the velocity components of x, y, and σ respectively;

[0097] Based on the above fourth-order ERK algorithm:

[0098] When predicting the future movement trajectory of the cold source blockage, input the initial position of the blockage, that is, the particle. According to the flow field data and time step calculated by the FVCOM model, through the following formula:

[0099] Next time point position = current position + current time point flow velocity × time step (8)

[0100] Calculate the positions of each particle at the next time step, and then by analogy, calculate the positions of each particle at multiple future time steps, so as to predict the future movement trajectory of the blockage;

[0101] When backtracking the movement trajectory before the blockage, input the position of the blockage at the current time point, the reverse flow velocity at the previous time point, and the time step. Through the following formula:

[0102] Position at the previous time point = Current position - Reverse flow velocity at the previous time point × Time step (9)

[0103] Calculate the positions of each particle at the previous time step, and so on, calculate the positions of each particle at multiple past time steps, so as to backtrack the source of the cold source blockage.

[0104] The offshore alarm module is connected to each module and issues an alarm according to the data anomaly instructions sent by each module.

[0105] The disaster-causing object monitoring daily report module is used to record the daily monitoring data of the red tide monitoring module, the pollution source prediction module, and the marine biological density monitoring module, analyze the data and judge anomalies, and generate an analysis report.

[0106] The cold source external risk early warning and forecasting system further includes a user management module and a role management module;

[0107] The user management module is used for user registration and assigns user query permissions;

[0108] The role management module is used to assign user editing permissions.

[0109] Embodiment 2

[0110] The construction process of the 4th-order ERK algorithm is as follows:

[0111] (1) Nonlinear system of ordinary differential equations (ODE)

[0112]

[0113] Where is the particle position at time t; is the rate of change of the particle position with time; is the three-dimensional velocity field generated by the model. This equation can be solved by any method that can solve nonlinear ODE’s. A common method for algorithm grouping is the Runge-Kutta (ERK) multiple setting method, which stems from solving the discrete integral:

[0114]

[0115] Assume is the position of the particle at t = t n moment, then the particle is at t = t n+1 (= t nPosition at time (+Δt) is determined by the following fourth-order four-stage ERK method:

[0116]

[0117] where Δt is the time step. In the calculation, since the velocity field is invariant within the tracking time interval Δt, the dependence of the velocity field on time is eliminated. In a multi-dimensional system, the local functional derivative of can be estimated by the exact auxiliary points in the (x, y, z) space

[0118] In the two-dimensional (x, y) plane, particles can be tracked by solving the x and y velocity equations

[0119]

[0120] The fourth-order ERK algorithm can be reduced to the following form:

[0121]

[0122] In three-dimensional (x, y) space, particles can be tracked by solving the x, y, and z velocity equations

[0123]

[0124] where u, v, and are the velocity components of x, y, and σ. The relationship between

[0125]

[0126] where w is the vertical velocity in the z-coordinate direction. Let In this case, the fourth-order ERK algorithm can be reduced to the following form:

[0127]

[0128]

Claims

1. A cold source external risk early warning and forecasting system, comprising a map calling module, a hydrodynamic model module, a pollution source prediction module, a red tide monitoring module, a marine garbage monitoring module, a marine biological density monitoring module, and a drift trajectory prediction module, characterized in that: The map calling module is used to call satellite map data and construct a monitoring area map; The hydrodynamic model module is used to call the chart data and tide data of the monitoring area, drive the water movement with the tide data at the model opening boundary based on the chart data, calculate the flow field data using the FVCOM model, and construct the flow field distribution map; The red tide monitoring module can detect the red tide in the area in time based on the real-time satellite image data and the red tide algae regular monitoring data. When the monitoring data exceeds the red tide outbreak threshold and may threaten the cold source, an alarm will be issued. The marine garbage monitoring module is based on the offshore fixed-point regular monitoring data and the coastal fixed-point regular monitoring data. When the amount of marine garbage at the monitoring point exceeds the standard and may threaten the cold source, an alarm is issued; The pollution source prediction module is based on the given pollution source input in advance, calls the flow field data calculated by the hydrodynamic model, and uses the DYE dyeing module in the FVCOM model to calculate and predict the diffusion range and diffusion concentration of pollutants after they are released from the source; when the predicted concentration of pollutants at the preset location exceeds the threshold, an alarm is issued; The marine biological density monitoring module inputs the monitoring data of multiple monitoring points set in the monitoring area, compares each monitoring data of the marine biological density with the preset parameters, and issues an alarm when the type and quantity of biological resources are abnormal; The drift trajectory prediction module is used to simulate the movement trajectory of cold source blockages. By calling the flow field data calculated by the hydrodynamic model and based on the Lagrangian particle tracking model in the FVCOM model, the future movement trajectory of the cold source blockage is predicted, or its movement route is traced back to track the source, and targeted measures are proposed according to preset specifications.

2. The cold source external risk early warning and forecasting system according to claim 1, characterized in that: The red tide monitoring and prediction module includes a red tide algae monitoring submodule, a buoy monitoring submodule, and a buoy alarm submodule; The red tide algae monitoring submodule inputs the red tide algae data according to the ocean satellite monitoring and daily manual sampling detection, and issues an alarm if the ocean satellite data indicates that a red tide may occur in the monitored area; if the red tide algae data detected by manual sampling exceeds the preset risk level threshold parameter, an alarm corresponding to the risk level is issued; The buoy monitoring submodule obtains the seawater biochemical indicators detected by the buoy sensors from each buoy set in the monitoring area through wireless communication, and compares them with the preset parameter thresholds. If the set parameter thresholds are exceeded, a risk level alarm is issued through the buoy alarm submodule.

3. The cold source external risk early warning and forecasting system according to claim 1, characterized in that: The marine biological density monitoring module includes a zooplankton monitoring submodule, a phytoplankton monitoring submodule, a benthic organism monitoring submodule, a streak net monitoring submodule, and a drift gillnet monitoring submodule; The zooplankton monitoring submodule is used to input sample monitoring data from each manual sampling point in the monitoring area, record the zooplankton density, and compare it with the preset risk level threshold parameters. When it is higher than the preset parameters, an alarm corresponding to the risk level is issued; The phytoplankton monitoring submodule is used to input sample monitoring data from each manual sampling point in the monitoring area, record the phytoplankton density, and compare it with the preset risk level threshold parameters. When it is higher than the preset parameters, an alarm corresponding to the risk level is issued; The benthic monitoring submodule is used to input sample monitoring data from each manual sampling point within the monitoring area, record the density of benthic organisms, and compare it with the preset risk level threshold parameters. When it is higher than the preset parameters, an alarm corresponding to the risk level is issued; The net-strapping monitoring submodule is used to set a net in the monitoring area, monitor the types and quantities of biological resources caught by the net through wireless communication and manual sampling, and monitor the caught marine garbage at the same time. The monitoring data is input into the net-strapping monitoring submodule and compared with the preset risk level threshold parameters. When it is higher than the preset parameters, an alarm corresponding to the risk level is issued; The drift net monitoring module is used to set up drift nets in the monitoring area, monitor the types and quantities of biological resources caught by the drift nets through wireless communication and manual sampling, and monitor the caught marine debris. The monitoring data is input into the drift net monitoring sub-module and compared with the preset risk level threshold parameters. When it is higher than the preset parameters, the corresponding risk level alarm is issued.

4. The cold source external risk early warning and forecasting system according to claim 3, characterized in that: The marine biological density monitoring module also includes a trawl monitoring submodule and a shipborne sonar monitoring submodule; The trawl monitoring submodule is used to record and monitor the types and quantity data of biological resources obtained through trawl fishing when patrol boats are patrolling and monitoring abnormalities. By comparing with the preset parameters, corresponding alarms are issued when abnormalities occur; The shipborne sonar monitoring submodule is used to obtain the type and density data of biological resources monitored by patrol ships through wireless communication, and to issue an alarm when the type and quantity of biological resources are abnormal.

5. The cold source external risk early warning and forecasting system according to claim 1, characterized in that: It also includes an offshore alarm module, which is connected to each module and issues an alarm according to data abnormality instructions sent by each module.

6. The cold source external risk early warning and forecasting system according to claim 1, characterized in that: It also includes a disaster-causing object monitoring daily report module, which is used to record the daily monitoring data of the red tide monitoring module, the pollution source prediction module, and the marine biological density monitoring module, and analyze and judge the abnormalities of the data to generate an analysis report.

7. The cold source external risk early warning and forecasting system according to claim 1, characterized in that: It also includes user management module and role management module; The user management module is used for user registration and allocating user query permissions; The role management module is used to assign user editing permissions.

8. The cold source external risk early warning and forecasting system according to claim 1, characterized in that: The specific process of predicting the future movement trajectory of the cold source blockage or tracing its movement route based on the Lagrangian particle tracking model in the FVCOM model is as follows: In three-dimensional (x,y,z) space, particles can be tracked by solving the x, y, and z velocity equations. Assumptions For the particle at t = t n The particle is at the time t = t n+1 (ie t n +Δt) time In three-dimensional space, it can be determined by the following fourth-order Runge-Kutta (ERK) algorithm: Among them, u, v, are the velocity components of x, y, and σ respectively; Based on the above 4th order ERK algorithm: When predicting the future movement trajectory of the cold source blockage, the initial position of the blockage, i.e., the particle, is input, and the flow field data and time step calculated by the FVCOM model are used through the following formula: Next time point position = current position + current time point velocity × time step (8) Calculate the position of each particle in the next time step, and then calculate the position of each particle in multiple future time steps in the same way, so as to predict the future movement trajectory of the blockage; When tracing back the movement trajectory of the blockage, the position of the blockage at the current time point, the reverse flow velocity at the previous time point, and the time step are input, and the following formula is used: Position at the previous time point = current position - reverse flow velocity at the previous time point × time step (9) The position of each particle in the previous time step is calculated, and then the position of each particle in the past multiple time steps is calculated by analogy, so as to trace back the source of the cold source blockage.

9. The cold source external risk early warning and forecasting system according to claim 1 or 8, characterized in that: The cold source blockages include red tides, marine garbage, and other marine organisms.

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