Lake basin ecological process coupling simulation system based on data fusion

Abstract

This invention relates to the field of lake basin simulation technology and discloses a coupled simulation system for lake basin ecological processes based on data fusion. The system includes: a spectral confusion potential generation module to generate a carbon-phosphorus co-emission spectral confusion potential field; a confusion-sensitive observation vector construction module to construct confusion-sensitive observation vectors; a state evolution equation system construction module to establish a state evolution equation system; a data fusion module to output the fused dissolved inorganic phosphorus and fused algal biomass; an algal bloom risk intensity calculation module to calculate the deconfused algal bloom risk intensity; and an algal bloom exposure flux calculation module to calculate the algal bloom exposure flux. This invention quantifies the spectral confusion effect caused by shoreline inundation by generating a carbon-phosphorus co-emission spectral confusion potential field; and by combining the state evolution equation system with data fusion technology, it calculates the algal bloom risk intensity while taking into account both the current algal population and its growth trend, providing a practical reference for lake basin ecological simulation and management decisions related to algal bloom control.

Description

Technical Field

[0001] This invention relates to the field of lake basin simulation technology, and more specifically, to a coupled simulation system for lake basin ecological processes based on data fusion. Background Technology

[0002] Simulation of lake basin ecological processes and risk assessment of algal blooms are core tasks of water environment management. Remote sensing technology, due to its wide coverage and efficient data acquisition, has become an indispensable technical support in this field. In the natural environment, lake water levels often fluctuate periodically with seasonal changes, extreme precipitation, or snowmelt, leading to repeated inundation of shoreline areas. Soils or sediments of different land cover types in the shoreline store large amounts of carbon and phosphorus, which release colored dissolved organic matter and dissolved inorganic phosphorus into the water after inundation. Cultivated land has the highest release potential due to long-term fertilization, followed by wetlands, while grasslands and bare land have relatively low release potential.

[0003] Colored dissolved organic matter and algal pigments exhibit significant spectral overlap in the remote sensing sensitive band of 400 to 900 nanometers, resulting in spectral confusion. Current simulation techniques fail to quantify this dynamic confusion effect, directly using raw multi-band remote sensing reflectance for ecological parameter inversion, thus failing to effectively distinguish the spectral signals of the two types of substances. Dissolved inorganic phosphorus is a key nutrient for algal growth, and algal biomass is a core indicator for algal bloom risk assessment; deviations in the estimation of these two parameters directly affect the accuracy of subsequent analyses. Furthermore, current techniques lack effective coupling between carbon and phosphorus co-release processes and ecological evolution models, often employing static boundaries or simple partitioning, which fails to reflect the spatiotemporal dynamics of the confusion potential field. This makes ecological process simulations unable to accurately reflect actual hydrological, topographical, and surface cover conditions, further exacerbating parameter estimation errors. Inaccurate ecological parameters and risk assessment results hinder reliable decision-making for lake algal bloom control and watershed ecological protection, restricting the scientific rigor and effectiveness of lake watershed ecological management and failing to meet the control requirements for water environment safety and ecological balance. Summary of the Invention

[0004] This invention provides a coupled simulation system for lake basin ecological processes based on data fusion, which solves the technical problems mentioned in the background.

[0005] This invention provides a data fusion-based coupled simulation system for lake basin ecological processes, comprising:

[0006] The spectral confusion potential field generation module calculates the inundation age based on the modulus of the surface elevation gradient and the water level process, and generates a carbon and phosphorus co-release spectral confusion potential field by combining the release coefficient of land cover type.

[0007] The module for constructing confused sensitive observation vectors converts multi-band remote sensing reflectance into confused sensitive observation vectors composed of spectral slope-type indicators and pigment peak-type indicators.

[0008] The state evolution equation system construction module establishes a state evolution equation system that includes dissolved inorganic phosphorus, colored soluble organic matter and algal biomass, and defines the carbon-phosphorus co-release spectral confusion potential field as the interfacial pulse release source term of dissolved inorganic phosphorus and colored soluble organic matter.

[0009] The data fusion module uses the carbon-phosphorus co-release spectral confusion potential field as the spatial structure regularization weight to perform data fusion on the confusion-sensitive observation vector and the state evolution equation set, and outputs the fused dissolved inorganic phosphorus and the fused algal biomass.

[0010] The algal bloom risk intensity calculation module calculates the net growth driving rate based on the dissolved inorganic phosphorus after fusion, and maps the product of the net growth driving rate and the algal biomass after fusion to the decongested algal bloom risk intensity.

[0011] The algal bloom exposure flux calculation module multiplies the decongested algal bloom risk intensity by the fused algal biomass to obtain the algal bloom exposure flux, and performs time integration on the algal bloom exposure flux to obtain the cumulative exposure.

[0012] The beneficial effects of this invention are as follows: This invention quantifies the spectral confusion effect caused by shoreline inundation by generating a carbon-phosphorus co-release spectral confusion potential field, and then converts multi-band remote sensing reflectance into targeted confusion-sensitive observation vectors. Combining state evolution equations and data fusion technology, and incorporating spatial structure regularization weights, it obtains dissolved inorganic phosphorus and algal biomass data that reflect the true state of the water body. The algal bloom risk intensity calculated based on these data takes into account both the current algal abundance and its growth trend, while the cumulative exposure reflects the total intensity of the algal bloom impact over a given period. This achieves effective coupling between ecological processes and spectral observations, providing a practical reference for lake basin ecological simulation and algal bloom control-related management decisions, adaptable to the topographic, hydrological, and surface cover characteristics of different watersheds. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of a lake basin ecological process coupling simulation system based on data fusion according to the present invention;

[0014] Figure 2 This is a schematic diagram of the computational logic of the present invention;

[0015] Figure 3 This is a schematic diagram of the computational scenario of the present invention.

[0016] In the figure: Spectral confusion potential generation module 101, confusion sensitive observation vector construction module 102, state evolution equation system construction module 103, data fusion module 104, algal bloom risk intensity calculation module 105, algal bloom exposure flux calculation module 106. Detailed Implementation

[0017] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" indicate that the element or object preceding the term encompasses the elements or objects listed following the term and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] like Figures 1-3 As shown, a coupled simulation system for lake basin ecological processes based on data fusion includes:

[0020] The spectral confusion potential field generation module 101 calculates the inundation age based on the modulus of the surface elevation gradient and the water level process, and generates a carbon and phosphorus co-release spectral confusion potential field by combining the release coefficient of the land cover type.

[0021] The confusion-sensitive observation vector construction module 102 converts multi-band remote sensing reflectance into a confusion-sensitive observation vector composed of spectral slope type index and pigment peak type index;

[0022] The state evolution equation set construction module 103 establishes a state evolution equation set including dissolved inorganic phosphorus, colored soluble organic matter and algal biomass, and defines the carbon-phosphorus co-release spectral confusion potential field as the interface pulse release source term of dissolved inorganic phosphorus and colored soluble organic matter.

[0023] Data fusion module 104 uses the carbon-phosphorus co-release spectral confusion potential field as the spatial structure regularization weight to perform data fusion on the confusion-sensitive observation vector and the state evolution equation set, and outputs the fused dissolved inorganic phosphorus and the fused algal biomass.

[0024] The algal bloom risk intensity calculation module 105 calculates the net growth driving rate based on the dissolved inorganic phosphorus after fusion, and maps the product of the net growth driving rate and the algal biomass after fusion to the decongested algal bloom risk intensity.

[0025] The algal bloom exposure flux calculation module 106 multiplies the decongested algal bloom risk intensity by the fused algal biomass to obtain the algal bloom exposure flux, and performs time integration on the algal bloom exposure flux to obtain the cumulative exposure.

[0026] In one embodiment of the present invention, the inundation age is calculated based on the modulus of the surface elevation gradient and the water level process, and a carbon and phosphorus co-release spectral confusion potential field is generated by combining the release coefficient of land cover type, specifically including:

[0027] The modulus of the surface elevation gradient is calculated based on the digital elevation model:

[0028] ;

[0029] in, The modulus of the surface elevation gradient. For spatial location The surface elevation at that location. This represents the spatial gradient of Earth's surface elevation. Represents the magnitude of the gradient;

[0030] Calculate the inundation start time:

[0031] ;

[0032] in, The start time of the flooding. For a moment The water level process, Indicates the removal of the definitive boundary;

[0033] Calculate the submersion age:

[0034] ;

[0035] in, For the age of submersion, This represents the maximum value between zero and the calculated value.

[0036] Generates a carbon-phosphorus co-release spectral confusion potential field:

[0037] ;

[0038] in, For the carbon-phosphorus co-release spectrum confusion potential field, For spatial location Land cover type at the location This represents the phosphorus release magnitude coefficient corresponding to the land cover type. This represents the carbon emission magnitude coefficient corresponding to the land cover type. For numerical stability, a small amount, The timescale of decay due to submersion age It is an exponential function.

[0039] It should be noted that a digital elevation model (DEM) is a topographic data model obtained by measuring the elevation of various points on the Earth's surface, reflecting the spatial distribution characteristics of elevation. It can be acquired through techniques such as aerial photogrammetry, satellite remote sensing, and ground-based lidar. Spatial elevation is the vertical height of the Earth's surface relative to a reference surface at a specific spatial location, reflecting the terrain's elevation at that location. The gradient of surface elevation with respect to a spatial location is the rate and direction of change of surface elevation at that location, reflecting the degree and trend of elevation change in space. The modulus of the surface elevation gradient is the magnitude of the spatial gradient of surface elevation, reflecting the steepness of the slope at that spatial location. Water level process values ​​are numerical sequences of the height of a lake's water surface relative to a reference surface at different times, reflecting the dynamic changes in lake water levels over time; they can be acquired through techniques such as real-time monitoring of water level stations, satellite remote sensing inversion, and hydrological model simulation. Inundation age reflects the actual duration of water cover at a specific spatial location. Land cover type at a spatial location is the type of land cover at that specific spatial location. The phosphorus release amplitude coefficient is the intensity coefficient of phosphorus release from soil or sediment corresponding to a specific land cover type, reflecting the differences in phosphorus release capacity among different land cover types. The carbon release amplitude coefficient is the intensity coefficient of carbon or colored dissolved organic matter release from soil or sediment corresponding to a specific land cover type. The inundation age decay timescale is a characteristic parameter controlling the rate of decay of the intensity of material release induced by inundation over time, reflecting the rate of depletion, refixation, or diffusion dilution of easily released substances. The decay term is a negative exponential function value of the ratio of inundation age to the inundation age decay timescale, reflecting the degree of decay of material release intensity with increasing inundation time. The numerator term reflects the potential intensity of carbon and phosphorus co-release under the combined effects of land cover type and inundation time. For numerical stability, small values ​​are preferred, ranging from 0.001 to 0.01, to ensure normal calculation even in areas with near-zero slope. The denominator term reflects the geometric amplification constraint of slope on the carbon and phosphorus co-release confusion potential field. The carbon and phosphorus co-release spectral confusion potential field is the quotient of the numerator and denominator terms, reflecting the potential intensity of carbon and phosphorus co-release at a specific spatiotemporal location causing confusion in spectral detection.

[0040] It should be noted that the phosphorus release amplitude coefficients corresponding to different land cover types are determined based on the soil phosphorus pool characteristics of each land cover type. Due to long-term fertilization, cultivated land has a high available phosphorus content in the soil, and the phosphorus release amplitude coefficient ranges from 1.0 to 1.5. Wetland soils are rich in organic matter, and the phosphorus release coefficient ranges from 0.8 to 1.2. Grassland soils have a moderate phosphorus pool content, and the coefficient ranges from 0.5 to 0.9. Bare land soils have a low phosphorus content, and the coefficient ranges from 0.1 to 0.4. Specifically, soil samples of different land cover types can be collected, and the phosphorus concentration released per unit time can be measured through indoor flooding release experiments. The corresponding coefficients can then be calibrated by combining the data with field measurements. The carbon emission amplitude coefficients corresponding to different land cover types are determined based on the soil organic matter content of each land cover type. Wetland soils have the highest organic matter content, with carbon emission amplitude coefficients ranging from 0.9 to 1.3; cultivated land is next, with values ​​ranging from 0.7 to 1.0; grassland has values ​​ranging from 0.4 to 0.8; and bare land has the lowest, with values ​​ranging from 0.08 to 0.3. Specifically, soil samples from different land cover types can be collected to determine the organic matter content and humus composition. The release of colored dissolved organic matter can be monitored through indoor simulated flooding experiments, and the corresponding coefficients can be calibrated by combining the data from field monitoring of colored dissolved organic matter concentrations in water bodies.

[0041] It should be noted that the decay timescale for the inundation age ranges from 3 to 15 days. For shallow lakes, due to active hydrodynamic conditions and rapid diffusion and dilution of substances, the decay timescale ranges from 3 to 8 days. For deep lakes, due to gentle hydrodynamic conditions and slow substance decay, the decay timescale ranges from 8 to 15 days. Specifically, monitoring points can be set up in different areas of the lake to continuously monitor the changes in the concentration of phosphorus and colored dissolved organic matter in the water after inundation. By fitting the concentration decay curve, the decay time constant can be obtained as the decay timescale for the inundation age. This invention quantifies the impact of surface slope, inundation duration, and land cover characteristics on carbon and phosphorus co-release using fundamental data such as digital elevation models, water level processes, and land cover types, constructing a spectral confusion potential field for carbon and phosphorus co-release. This transforms the carbon and phosphorus co-release process induced by shoreline inundation into a calculable continuous field, clarifying the spatiotemporal distribution characteristics of the confusion potential. This provides structured prior information for subsequent spectral de-confusing, avoiding the limitations of traditional static boundaries or simple zoning. It ensures that the calculation of the confusion potential field is based on actual topography, hydrology, and land cover conditions, improving the reliability of subsequent data fusion and algal bloom risk assessment, and providing accurate quantitative basis for the coupled simulation of lake basin ecological processes.

[0042] In one embodiment of the present invention, converting multi-band remote sensing reflectance into a confusion-sensitive observation vector composed of spectral slope-type indices and pigment peak-type indices specifically includes:

[0043] Calculate spectral slope type indices:

[0044] ;

[0045] in, It is a spectral slope type index. For spatial location, For time, This represents the multi-band remote sensing reflectance corresponding to the first wavelength. This represents the multi-band remote sensing reflectance corresponding to the second wavelength. The operator for natural logarithms;

[0046] Calculate pigment peak shape index:

[0047] ;

[0048] in, As an indicator of pigment peak shape, This represents the multi-band remote sensing reflectance corresponding to the fourth wavelength. This represents the multi-band remote sensing reflectance corresponding to the third wavelength;

[0049] Constructing a confusingly sensitive observation vector:

[0050] ;

[0051] in, To obfuscate sensitive observation vectors.

[0052] It should be noted that the preset observation wavelength set is a pre-defined collection of multiple remote sensing observation wavelengths, reflecting the wavelength range available for spectral detection. The preferred value is 400 nm to 900 nm, which covers the light absorption and reflection sensitive bands of colored dissolved organic matter and algal pigments in water, encompassing key blue, green, red, and near-infrared spectral regions. The first wavelength is a short-wavelength selected from the preset observation wavelength set that is sensitive to colored dissolved organic matter, reflecting the selection of observation wavelengths targeting the absorption characteristics of colored dissolved organic matter. The preferred value is 440 nm to 480 nm, representing the strong absorption characteristics of colored dissolved organic matter in the blue-violet band; for example, a value of 460 nm could be used. The second wavelength is a short-wavelength selected from the preset observation wavelength set that complements the first wavelength, reflecting the auxiliary observation wavelength used to construct spectral slope-type indicators. The preferred value is 500 nm to 550 nm, meaning that the absorption intensity of colored dissolved organic matter in this band differs significantly from the first wavelength, forming an effective slope; for example, a value of 530 nm could be used. Multi-band remote sensing reflectance is the ratio of the water-leaving radiance received by the remote sensing sensor at different wavelengths to the incident irradiance, reflecting the water body's reflection characteristics of light at different wavelengths. The third wavelength is a wavelength selected from a preset set of observation wavelengths that is sensitive to algal pigment absorption, reflecting the selection of observation wavelengths targeting algal pigment characteristics; a preferred value is 620 nm to 660 nm, which corresponds to the absorption peak region of algal chlorophyll in the red light band, for example, 650 nm. The fourth wavelength is a wavelength selected from the preset set of observation wavelengths that complements the third wavelength, reflecting the auxiliary observation wavelength for constructing pigment peak shape indicators; a preferred value is 680 nm to 720 nm, which corresponds to the sensitive region of the red-edge effect of algal pigments, creating a difference in absorption and reflection with the third wavelength, for example, 700 nm. The pigment peak shape indicator is a quantitative indicator corresponding to the above differences, reflecting the sensitive characteristics of changes in algal biomass and pigment content in the water body. The confusion-sensitive observation vector is a column vector containing spectral slope-type indicators and pigment peak-type indicators, reflecting a comprehensive observation carrier that simultaneously characterizes changes in colored soluble organic matter and algal pigments.

[0053] It should be noted that the specific selection criteria for the first and second wavelengths are as follows: the first wavelength is located between 440 and 480 nm, the second wavelength is located between 500 and 550 nm, and the interval between the two wavelengths is not less than 40 nm; the sensitivity threshold is that when the concentration of colored soluble organic matter changes by 1 mg / L, the difference in the natural logarithm of the reflectance of the two wavelengths changes by not less than 0.1. For example, selecting a first wavelength of 460 nm and a second wavelength of 530 nm, with an interval of 70 nm, meets the selection criteria and can sensitively respond to changes in colored soluble organic matter. The specific selection criteria for the third and fourth wavelengths are as follows: the third wavelength is located between 620 and 660 nm (chlorophyll absorption peak), the fourth wavelength is located between 680 and 720 nm (red edge effect region), and the interval between the two wavelengths is not less than 30 nm; the sensitivity threshold is that when the chlorophyll concentration changes by 1 μg / L, the difference in the natural logarithm of the reflectance of the two wavelengths changes by not less than 0.05; for example, selecting a third wavelength of 650 nm and a fourth wavelength of 700 nm, with an interval of 50 nm, meets the selection criteria and can sensitively respond to changes in algal pigments. The number of bands for multi-band remote sensing reflectance should be no less than 10, and they should be densely distributed in the 400-550 nm and 620-720 nm ranges (one band every 10 nm), while other ranges can be more sparse (one band every 20 nm). Furthermore, multi-band remote sensing reflectance can be preprocessed. Data preprocessing requirements include atmospheric correction, Rayleigh scattering correction, and solar flare removal, with the reflectance error after correction not exceeding 5%. After preprocessing, the band data needs to be interpolated to wavelength points of a preset observation wavelength set to ensure accurate matching with the selected wavelengths; details are omitted here.

[0054] It should be noted that this invention addresses the problem of spectral signal confusion between colored soluble organic matter and algal pigments caused by carbon and phosphorus co-release. It selects targeted wavelength combinations from a pre-defined set of observation wavelengths, constructs indices sensitive to both types of substances using the natural logarithmic difference, and then combines these indices into a confusion-sensitive observation vector. This transforms the original multi-band remote sensing reflectance into a mechanism-guided low-dimensional observation carrier, pinpointing the two core sources of confusion. This avoids the conflation of the two types of signals by traditional observation indices, providing a clear observational basis for signal separation in subsequent data fusion processes. This ensures a precise correspondence between observational data and the confusion mechanism, improving the reliability of subsequent ecological process coupling simulations.

[0055] In one embodiment of the present invention, a set of state evolution equations is established, including dissolved inorganic phosphorus, colored soluble organic matter, and algal biomass, and the carbon-phosphorus co-release spectral confusion potential field is defined as the interfacial pulse release source term of dissolved inorganic phosphorus and colored soluble organic matter, specifically including:

[0056] Define the state variable field:

[0057] ;

[0058] in, For a state variable field, To determine the concentration of dissolved inorganic phosphorus, The equivalent absorption intensity of colored soluble organic matter. Algal biomass;

[0059] Establish the evolution equation for dissolved inorganic phosphorus:

[0060] ;

[0061] in, The maximum growth rate. It is the half-saturation constant. The diffusion coefficient for dissolved inorganic phosphorus. For the flow field velocity, This is a source of dissolved inorganic phosphorus flowing into the lake from the watershed. This is an interfacial pulse release source term for dissolving inorganic phosphorus;

[0062] Establish the evolution equation of equivalent absorption intensity for colored soluble organic compounds:

[0063] ;

[0064] in, The decay coefficient of colored soluble organic matter. The diffusion coefficient of colored soluble organic compounds. For the colored dissolved organic matter source term input to the watershed, This is an interfacial pulse release source term for colored soluble organic matter;

[0065] Establish an equation for the evolution of algal biomass:

[0066] ;

[0067] in, This is the algal biomass loss coefficient. The diffusion coefficient of algal biomass;

[0068] Define the interface pulse release source term:

[0069] ;

[0070] ;

[0071] in, The proportionality coefficient for the pulsed release of dissolved inorganic phosphorus at the interface. This represents the proportionality coefficient for the interfacial pulse release of colored soluble organic compounds. The spectral confusion potential field for carbon-phosphorus co-release.

[0072] It should be noted that dissolved inorganic phosphorus concentration refers to the amount of inorganic phosphorus in the water that can be directly absorbed and utilized by algae. The equivalent absorption intensity of colored dissolved organic matter (CDM) is a quantitative indicator characterizing the light absorption capacity of CDM. Algal biomass is the total amount of algal population in the water, reflecting the growth and reproduction status of algae and the basis of potential algal bloom risk. The state variable field is a comprehensive variable system including dissolved inorganic phosphorus concentration, equivalent absorption intensity of CDM, and algal biomass. The diffusion term is a quantitative term characterizing the migration of substances in the water due to molecular diffusion and turbulent diffusion, reflecting the trend of homogenization in the spatial distribution of substances. The convection term is a quantitative term characterizing the migration of substances with the flow of water, reflecting the transport effect of the flow field on the spatial distribution of substances. The dissolved inorganic phosphorus source term entering the lake from the watershed is the amount of dissolved inorganic phosphorus input from the watershed into the lake, reflecting the contribution of exogenous phosphorus to the replenishment of nutrients in the lake; it can be collected through sampling analysis at river monitoring stations within the watershed, estimation using non-point source pollution models, and flux monitoring at the lake inlet. The interfacial pulse release source term for dissolved inorganic phosphorus is the amount of dissolved inorganic phosphorus released pulse by the lake sediment-water interface into the water body, reflecting the intensity of sudden replenishment of endogenous phosphorus. The phosphorus uptake term by algae is a quantitative term for the absorption and utilization of dissolved inorganic phosphorus by algae, reflecting the degree of nutrient consumption by algal growth.

[0073] It should be noted that the colored dissolved organic matter (DOC) source term from the watershed refers to the amount of DOC entering the lake from the watershed, reflecting the degree of external influence on the optical background of the water body. This term can be collected through techniques such as river water quality monitoring within the watershed, wetland effluent sampling and analysis, and simulation of terrestrial organic matter loss models. The interfacial pulse release source term for DOC is the amount of DOC released pulse-like from the lake sediment-water interface into the water body, reflecting the intensity of sudden disturbances to the optical background of the water body from internal sources. The natural decay term is the reduction of DOC due to photodegradation, flocculation, and sedimentation, reflecting its natural rate of disappearance in the water body. The phosphorus-driven growth term is a quantitative term for algal growth dependent on dissolved inorganic phosphorus, reflecting the driving effect of nutrients on algal reproduction. The biomass loss term is the reduction of algal biomass due to death, sedimentation, and feeding, reflecting the natural rate of algal biomass consumption. The proportionality coefficient for pulsed release at the dissolved inorganic phosphorus interface is a parameter that converts the confusion potential field of the carbon-phosphorus co-release spectrum into the amount of dissolved inorganic phosphorus released at the interface. It reflects the quantitative correlation between the confusion potential field and the actual phosphorus release, and its preferred value is 0.01 to 0.1 mg / m² / day. The proportionality coefficient for pulsed release at the colored soluble organic matter interface is a parameter that converts the confusion potential field of the carbon-phosphorus co-release spectrum into the amount of colored soluble organic matter released at the interface. It reflects the quantitative correlation between the confusion potential field and the actual carbon release, and its preferred value is 0.008 to 0.08 absorption coefficient units / m² / day.

[0074] It should be noted that the diffusion coefficient for dissolved inorganic phosphorus is preferably set to 1×10⁻⁶ to 5×10⁻⁶ square meters per second, the diffusion coefficient for colored dissolved organic matter is preferably set to 8×10⁻⁷ to 4×10⁻⁶ square meters per second, and the diffusion coefficient for algal biomass is preferably set to 5×10⁻⁷ to 3×10⁻⁶ square meters per second. These values ​​can be set based on measured statistical data of water diffusion coefficients, combined with the molecular mass and particle characteristics of the substances; the smaller the molecular mass and the smaller the particle size, the larger the diffusion coefficient. Simultaneously, lake depth and hydrodynamic activity should be considered; values ​​are tended to be higher for shallow, turbulent lakes and lower for deep, calm lakes. Methods for obtaining flow field velocities in the convection term include in-situ monitoring using an Acoustic Doppler Current Profiler (ADCP), three-dimensional hydrodynamic model simulation, and remote sensing inversion of the flow field.

[0075] It should be noted that the decay coefficient for colored dissolved organic matter in the natural decay term is preferably set to 0.01 to 0.05 per day, which can be determined based on experimental data of colored dissolved organic matter decay under different light conditions and water temperatures. The stronger the light intensity and the higher the water temperature, the larger the decay coefficient. For example, the decay coefficient for surface water in summer can be set to 0.04 per day, and for bottom water in winter, it can be set to 0.015 per day. In the biomass loss term, the algal biomass loss coefficient is preferably set to 0.02 to 0.1 per day. This can be determined based on measured data of algal settling rate and mortality rate, as well as the calibration results of ecological model parameters. The larger the algal cells and the weaker the water disturbance, the greater the settling loss and the higher the loss coefficient. For example, the loss coefficient can be set to 0.03 per day during cyanobacterial blooms and 0.07 per day when diatoms are dominant. The dissolved inorganic phosphorus ratio coefficient ranges from 0.01 to 0.1 mg / m² / day, and the colored dissolved organic matter ratio coefficient ranges from 0.008 to 0.08 absorption coefficient units / m² / day. The calibration method employs a data assimilation approach, combining measured concentration data of dissolved inorganic phosphorus and colored dissolved organic matter in lake water to inversely optimize the coefficient values. The calibration is based on the principle that the relative error between the model-simulated concentration value and the measured value after coefficient calibration does not exceed 15%.

[0076] In one embodiment of the present invention, the carbon-phosphorus co-release spectral confusion potential field is used as the spatial structure regularization weight to perform data fusion on the confusion-sensitive observation vector and the state evolution equation system, outputting the fused dissolved inorganic phosphorus and the fused algal biomass, specifically including:

[0077] Define the observation operator:

[0078] ;

[0079] in, For the observation operator, For a state variable field, The equivalent absorption intensity of colored soluble organic matter. For algal biomass, The linear coefficient representing the equivalent absorption intensity of colored soluble organic compounds. The linear coefficient of algal biomass;

[0080] Construct the objective function:

[0081] ;

[0082] in, Let be the objective function. To obfuscate sensitive observation vectors, Let be the inverse of the observation error covariance matrix. The normal intensity coefficient, For the carbon-phosphorus co-release spectrum confusion potential field, To determine the concentration of dissolved inorganic phosphorus, For gradient operators, Indicates the modulus;

[0083] Solving for the optimal state variable field:

[0084] ;

[0085] ;

[0086] in, For the optimal state variable field, This represents the variable corresponding to the minimum value. To dissolve inorganic phosphorus after fusion, This represents the biomass of the fused algae.

[0087] It should be noted that the observation operator is a function that maps the state variable field to the observation space, reflecting the quantitative correlation between the state variables (equivalent absorption intensity of colored dissolved organic matter, algal biomass) and the observed signal. The first linear coefficient is the contribution coefficient of the equivalent absorption intensity of colored dissolved organic matter to the first component of the observation operator, reflecting the influence weight of this state variable on the spectral slope type index; the preferred value is 0.1 to 0.8, based on water body optical observation experimental data, obtained by fitting the correlation between colored dissolved organic matter concentration and spectral slope type index, for example, a value of 0.5 can be used for eutrophic lakes. The second linear coefficient is the contribution coefficient of algal biomass to the first component of the observation operator, reflecting the influence weight of this state variable on the spectral slope type index; the preferred value is 0.05 to 0.6, based on the measured correlation data between algal biomass and spectral slope type index, for example, a value of 0.3 can be used when cyanobacteria are dominant. The third linear coefficient is the contribution coefficient of the equivalent absorption intensity of colored dissolved organic matter to the second component of the observation operator, reflecting the weight of this state quantity's influence on the pigment peak shape index; the preferred value is 0.08 to 0.7, based on the coupled observation data of colored dissolved organic matter and pigment peak shape index, for example, a value of 0.4 can be taken for water bodies around wetlands. The fourth linear coefficient is the contribution coefficient of algal biomass to the second component of the observation operator, reflecting the weight of this state quantity's influence on the pigment peak shape index; the preferred value is 0.2 to 0.9, based on the measured calibration data of algal pigment content and pigment peak shape index, for example, a value of 0.7 can be taken when the chlorophyll concentration is high.

[0088] It should be noted that the objective function is a comprehensive quantitative function that measures the degree of observation fit and the rationality of the spatial structure of state variables, reflecting the optimization objective of the data fusion process. The observation fit term quantifies the difference between the output of the observation operator and the confusion-sensitive observation vector, reflecting the degree of fit between the model prediction and the actual observation. The difference vector is a vector composed of the element-wise differences between the output of the observation operator and the confusion-sensitive observation vector, reflecting the prediction error in a single observation dimension. The observation error covariance matrix is ​​a matrix characterizing the magnitude and correlation of observation errors, reflecting the reliability and error structure of the observation data; it is preferably a diagonal matrix with diagonal elements ranging from 0.001 to 0.01, based on the measured noise level of the remote sensing observation data; for example, a value of 0.005 can be used for multispectral satellite observations. The weighted norm squared is the norm squared of the difference vector under the weights of the observation error covariance matrix, reflecting the comprehensive observation error after considering error characteristics. The spatiotemporal domain integral is a mathematical operation that accumulates quantitative indicators within the spatiotemporal range, reflecting the overall effect of the indicators throughout the entire research spatiotemporal period. The spatial structure regularization term constrains the spatial distribution rationality of state variables, reflecting the spatial evolution laws that state variables should follow. The squared magnitude of the spatial gradient of dissolved inorganic phosphorus concentration is the square of the spatial gradient of dissolved inorganic phosphorus concentration, reflecting the drastic spatial variation of this state variable. The squared magnitude of the spatial gradient of the equivalent absorption intensity of colored dissolved organic matter is the square of the spatial gradient of this state variable, reflecting the degree of non-uniformity in its spatial distribution. The regularization strength coefficient is a parameter balancing the weights of the observation fitting term and the spatial structure regularization term, reflecting the importance of structural constraints in data fusion; it is preferably set between 0.01 and 0.1, based on the cross-validation results of data fusion, ensuring a balance between observation fit and structural rationality. For example, a value of 0.05 can be used in regions with moderate confusion. The optimal state variable field is the state variable field that minimizes the objective function, reflecting the optimal state estimate after fusing observation data and dynamic constraints. The fused dissolved inorganic phosphorus is the dissolved inorganic phosphorus concentration estimate extracted from the optimal state variable field, reflecting the true nutrient supply level of the water body after decontamination. The merged algal biomass is an estimate of algal biomass extracted from the optimal state variable field, reflecting the true algal growth status of the water body after deconfusion.

[0089] It should be noted that the time step for spatiotemporal domain integration is preferably set to 1 day, and the spatial grid size ranges from 100m×100m to 500m×500m, adjusted according to the size of the study area. A rectangular integration method is used, accumulating over time by the time step and by grid area weighting in space, which will not be elaborated further here. This invention uses the carbon-phosphorus co-emission spectral confusion potential as the spatial structure regularization weight to construct an objective function that fuses observational fitting and process constraints. Under the constraints of the kinetic equations, the optimal state variable field is solved to extract core ecological parameters. This integrates the structural characteristics of ecological processes into data fusion, and the dynamic regularization weight alleviates the observational non-uniqueness in highly confused areas. The fused dissolved inorganic phosphorus and algal biomass both closely match actual observations and conform to ecological evolution laws, avoiding biases from single data or models. This provides reliable basic data for subsequent algal bloom risk assessment and improves the rationality and practicality of the entire simulation system.

[0090] In one embodiment of the present invention, the net growth driving rate is calculated based on the dissolved inorganic phosphorus after fusion, and the product of the net growth driving rate and the algal biomass after fusion is mapped to the decongested algal bloom risk intensity, specifically including:

[0091] Calculate the net growth driver:

[0092] ;

[0093] in, Net growth driver The maximum growth rate. To dissolve inorganic phosphorus after fusion, It is the half-saturation constant. This is the loss coefficient;

[0094] Calculate the risk intensity of decongestion algal blooms:

[0095] ;

[0096] ;

[0097] in, It is the product of the merged algal biomass and the net growth driver rate. The combined algal biomass To clarify the intensity of algal bloom risk, It is an exponential function.

[0098] It should be noted that the numerator of the monotonic saturated growth term is the product of the maximum specific growth coefficient and the dissolved inorganic phosphorus after fusion, reflecting the potential driving contribution of phosphorus nutrients to algal growth. The denominator of the monotonic saturated growth term is the sum of the half-saturation constant and the dissolved inorganic phosphorus after fusion, reflecting the quantitative threshold of phosphorus nutrient limitation on algal growth. The net growth driving rate is the difference between the above quotient and the loss coefficient, reflecting the comprehensive growth potential of algae under the combined effects of nutrient supply and natural depletion. The decongested algal bloom risk intensity is an indicator after coupled calculation and normalization, reflecting the true risk level of algal blooms after eliminating spectral confusion interference.

[0099] It should be noted that the maximum specific growth coefficient for cyanobacteria is preferably taken as 0.2 to 0.5 per day, for diatoms as 0.15 to 0.4 per day, and for green algae as 0.18 to 0.45 per day. The determination is based on indoor pure culture experimental data and field monitoring results of dominant algae growth. The growth rate of cyanobacteria is generally higher than that of diatoms. The diatom coefficient is lower in clean water bodies and higher in eutrophic water bodies. For example, Microcystis aeruginosa (cyanobacteria) can be taken as 0.35 per day, and Cyclocystis jirovecii (diatom) can be taken as 0.25 per day. The preferred half-saturation constant for oligotrophic lakes is 0.01 to 0.03 mg / L, for mesotrophic lakes it is 0.03 to 0.08 mg / L, and for eutrophic lakes it is 0.08 to 0.2 mg / L. The fit is that the higher the background nutrient level, the larger the half-saturation constant, because algae adapt to high phosphorus environments over a long period of time, and their phosphorus saturation threshold increases. For example, 0.02 mg / L can be used for oligotrophic alpine lakes, and 0.15 mg / L can be used for eutrophic urban lakes. The base value of the loss coefficient is 0.02 to 0.1 per day. The correlation rules are as follows: when the temperature increases by 10 degrees Celsius, the loss coefficient increases by 0.01 to 0.02; when the light intensity is below 20,000 lux, the loss coefficient increases by 0.01 to 0.03; when the water disturbance intensity (flow velocity is greater than 0.1 m / s), the loss coefficient increases by 0.02 to 0.04. The adjustment is based on the influence of environmental factors on the algal settling and mortality rate. For example, the loss coefficient can be taken as 0.07 per day when the temperature is high in summer (30 degrees Celsius) and 0.03 per day when the temperature is low in winter (5 degrees Celsius).

[0100] It should be noted that this invention characterizes the limiting effect of phosphorus on algae through a monotonically saturated growth term, constructs a net growth driving rate by combining it with natural losses, and then couples it with the current algal biomass. After normalization mapping, the algal bloom risk intensity is obtained. This avoids the limitations of traditional single indicators (such as using only algal biomass) that ignore growth potential, and also eliminates the interference of spectral confusion on state quantities. The risk intensity reflects both the current algal biomass base and the future growth trend, and the results are more in line with the actual lake ecology. At the same time, the normalization process makes the risk values ​​comparable, providing standardized and reliable basic data for subsequent exposure flux assessment, and improving the logic and practicality of algal bloom risk assessment.

[0101] In one embodiment of the present invention, the algal bloom exposure flux is obtained by multiplying the decongested algal bloom risk intensity by the fused algal biomass, and the cumulative exposure is obtained by performing time integration on the algal bloom exposure flux, specifically including:

[0102] Calculate algal bloom exposure flux:

[0103] ;

[0104] in, For algal bloom exposure flux, The combined algal biomass To deconstruct the risk intensity of algal blooms;

[0105] Calculate cumulative exposure:

[0106] ;

[0107] in, To accumulate exposure, The starting time for integration. The end time of the integration process. The integral symbol is used. For time derivative.

[0108] It should be noted that the algal bloom exposure flux is the product of the merged algal biomass and the decongested algal bloom risk intensity, reflecting the risk-weighted impact intensity of the algal bloom per unit area and per unit time. The integration start time is the set starting point for the time integration of the algal bloom exposure flux, reflecting the starting point for the cumulative exposure assessment. A preferred value is May 1st each year, based on the typical start time of the peak algal bloom season in temperate lakes, when water temperature rises above 15 degrees Celsius and algae begin to reproduce rapidly; this value can be used for lakes in the middle and lower reaches of the Yangtze River. The integration end time is the set ending point for the time integration of the algal bloom exposure flux, reflecting the ending point for the cumulative exposure assessment. A preferred value is October 31st each year, based on the typical end time of the peak algal bloom season in temperate lakes, when water temperature drops below 15 degrees Celsius and algal growth rates decrease significantly; this value can be used for lakes in North China. The time integration calculation is the cumulative calculation process of the algal bloom exposure flux within the interval from the integration start time to the end time, reflecting the continuous cumulative effect of algal bloom exposure during the period. Cumulative exposure is the result of time integration, reflecting the total intensity of the impact of the deconfusioned algal bloom risk on a specific spatial location over the entire assessment period.

[0109] It should be noted that the unit of algal bloom exposure flux is defined as milligrams per square meter per day (mg / m² / day). The quantification standard is the product of the merged algal biomass unit (mg / m²) and the decongested algal bloom risk intensity (dimensionless). For example, if the merged algal biomass is 180 mg / m² and the risk intensity is 0.5, the corresponding algal bloom exposure flux is 90 mg / m² / day. This unit can intuitively reflect the risk-weighted algal biomass level within a unit of time and space. The selection of the integration start and end times is primarily based on the peak algal bloom period, supplemented by hydrological periods and seasons. The peak algal bloom period is determined based on historical algal bloom monitoring data of the lake. When the frequency of algal bloom occurrence exceeds 60% for a certain period over three consecutive years, it is defined as a peak period. For example, for subtropical lakes such as Taihu Lake, the integration start time is May 1st and the end time is October 31st; for temperate lakes in Northeast China, the start time is June 1st and the end time is September 30th, which aligns with local water temperature and algal bloom growth patterns. Simpson's integral method is preferred for time integration calculations. In addition, the grading assessment criteria for cumulative exposure are determined based on the threshold of the impact of cumulative exposure on aquatic ecosystems. Low exposure level is defined as cumulative exposure of less than 500 mg / m², medium exposure level is defined as 500 to 1500 mg / m², and high exposure level is defined as greater than 1500 mg / m². For example, the cumulative exposure in the nearshore area of ​​a lake is 1800 mg / m², which is considered high exposure level and requires algae control measures; while the cumulative exposure in the central area of ​​the lake is 400 mg / m², which is considered low exposure level and can be maintained through routine monitoring.

[0110] It should be noted that this invention is based on the deconfusioned algal bloom risk intensity and algal biomass. By coupling the algal bloom exposure flux, it is then converted into cumulative exposure through time integration, thereby quantifying the algal bloom risk from instantaneous intensity to cumulative effect over a period of time. This avoids the limitation that a single instantaneous indicator cannot reflect the continuous impact of risk. The exposure flux takes into account both the algal biomass base and risk weight, and the cumulative exposure can intuitively reflect the total intensity of algal bloom impact on a certain area during the assessment period. This provides a time-specific and quantitative decision-making basis for lake algal bloom control and ecological environment management, making risk assessment more in line with actual application scenarios and improving the practicality and guidance of assessment results.

[0111] It should be noted that, in actual deployment, this invention first conducts multi-type data collection. A digital elevation model of the lake basin is obtained through satellite remote sensing and ground-based lidar measurements. Water level monitoring stations are deployed at the lake inlet and center to record water level processes. Land cover types are interpreted based on high-resolution satellite imagery. Remote sensing reflectance is obtained using multi-band satellite and ground spectral observation points. Water samples are collected at monitoring sections of rivers flowing into the lake, and the watershed source term is calculated. On the hardware side, the system is deployed on a local monitoring center server cluster, with high-performance computing nodes connected to real-time data transmission interfaces. The software is adapted to the operating system and compilation environment, deploying various functional modules and building a user interface. System parameters are calibrated based on local historical data and observation results to ensure adaptation to watershed characteristics. Then, daily automatic tasks are set up to update data and perform calculations. Regular hardware maintenance and quarterly parameter recalibration ensure long-term stable operation.

[0112] It should be noted that, taking the coupled simulation of ecological processes in the Qinghai Lake Basin as an example, the process first involves using a digital elevation model to calculate the shoreline slope, determining the inundation initiation time and inundation age in different areas, based on the water level rise caused by spring snowmelt in Qinghai Lake. Then, based on the local grassland-wetland-dominated land cover type, the release coefficient is matched, and the inundation age decay timescale is adjusted to generate a spectral confusion potential field. Targeted atmospheric correction is applied to the satellite remote sensing reflectance, and a confusion-sensitive observation vector is constructed by selecting a specific wavelength range. Considering the low water temperature of Qinghai Lake, relevant ecological parameters are adjusted, transforming the confusion potential field into an interface pulse release source term, and incorporating it into the evolution equations of three types of state variables to construct dynamic constraints. Data fusion is performed using the confusion potential field as a regularized weight, extracting the deconfused dissolved inorganic phosphorus and algal biomass, and calculating the net growth driver rate and algal bloom risk intensity based on local environmental conditions. An integration period is set according to the high algal bloom season in Qinghai Lake from June to September, and exposure flux and cumulative exposure are calculated and classified, ultimately achieving the monitoring and assessment of the algal bloom process in the basin.

[0113] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0114] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A coupled simulation system for lake basin ecological processes based on data fusion, characterized in that, include: The spectral confusion potential field generation module calculates the inundation age based on the modulus of the surface elevation gradient and the water level process, and generates a carbon and phosphorus co-release spectral confusion potential field by combining the release coefficient of land cover type. The module for constructing confused sensitive observation vectors converts multi-band remote sensing reflectance into confused sensitive observation vectors composed of spectral slope-type indicators and pigment peak-type indicators. The state evolution equation system construction module establishes a state evolution equation system that includes dissolved inorganic phosphorus, colored soluble organic matter and algal biomass, and defines the carbon-phosphorus co-release spectral confusion potential field as the interfacial pulse release source term of dissolved inorganic phosphorus and colored soluble organic matter. The data fusion module uses the carbon-phosphorus co-release spectral confusion potential field as the spatial structure regularization weight to perform data fusion on the confusion-sensitive observation vector and the state evolution equation set, and outputs the fused dissolved inorganic phosphorus and the fused algal biomass. The algal bloom risk intensity calculation module calculates the net growth driving rate based on the dissolved inorganic phosphorus after fusion, and maps the product of the net growth driving rate and the algal biomass after fusion to the decongested algal bloom risk intensity. The algal bloom exposure flux calculation module multiplies the decongested algal bloom risk intensity by the fused algal biomass to obtain the algal bloom exposure flux, and performs time integration on the algal bloom exposure flux to obtain the cumulative exposure.

2. The lake basin ecological process coupling simulation system based on data fusion according to claim 1, characterized in that, The surface elevation of a spatial location is determined based on the digital elevation model, the gradient of the surface elevation with respect to the spatial location is calculated, and the magnitude of the gradient is taken as the magnitude of the surface elevation gradient. Select the time set where the water level value is not less than the surface elevation value, and take the infimum of the time set as the inundation start time; Calculate the difference between the current time and the inundation start time, and take the maximum value between the difference and zero as the inundation age; The corresponding phosphorus emission amplitude coefficient and carbon emission amplitude coefficient are determined based on the land cover type of spatial location.

3. The lake basin ecological process coupling simulation system based on data fusion according to claim 2, characterized in that, The negative exponential function value of the ratio of the inundation age to the inundation age decay timescale is used as the decay term. The phosphorus release amplitude coefficient, carbon release amplitude coefficient and decay term are multiplied together to obtain the numerator term. The modulus of the surface elevation gradient and the numerical stability small quantity are added together to obtain the denominator term. The quotient of the numerator term divided by the denominator term is calculated and set as the carbon and phosphorus co-emission spectral confusion potential field.

4. The lake basin ecological process coupling simulation system based on data fusion according to claim 1, characterized in that, Select the first wavelength and the second wavelength from the preset observation wavelength set, obtain the multi-band remote sensing reflectance corresponding to the spatial location and time, calculate the difference between the natural logarithm of the multi-band remote sensing reflectance corresponding to the first wavelength and the natural logarithm of the multi-band remote sensing reflectance corresponding to the second wavelength, and set the difference as a spectral slope type index. Select the third and fourth wavelengths from the preset observation wavelength set, obtain the multi-band remote sensing reflectance corresponding to the spatial location and time, calculate the difference between the natural logarithm of the multi-band remote sensing reflectance corresponding to the fourth wavelength and the natural logarithm of the multi-band remote sensing reflectance corresponding to the third wavelength, and set the difference as the pigment peak type index. Construct a column vector containing two components. Assign the spectral slope index to the first component of the column vector and the pigment peak index to the second component of the column vector to obtain the confusion-sensitive observation vector.

5. The lake basin ecological process coupling simulation system based on data fusion according to claim 1, characterized in that, Define the concentration of dissolved inorganic phosphorus, the equivalent uptake intensity of colored dissolved organic matter, and algal biomass, and combine the three to form a state variable field; An evolution equation for dissolved inorganic phosphorus was constructed, which states that the rate of change of dissolved inorganic phosphorus concentration over time is equal to the sum of the diffusion term, convection term, dissolved inorganic phosphorus source term entering the lake from the watershed, and the interfacial pulse release source term of dissolved inorganic phosphorus, minus the phosphorus uptake term by algae. An evolution equation for the equivalent absorption intensity of colored soluble organic matter was constructed. The equation states that the rate of change of the equivalent absorption intensity of colored soluble organic matter over time is equal to the sum of the diffusion term, the convection term, the source term of colored soluble organic matter input into the watershed, and the interfacial pulse release source term of colored soluble organic matter, minus the natural decay term.

6. A coupled simulation system for lake basin ecological processes based on data fusion according to claim 5, characterized in that, An algal biomass evolution equation was constructed, which states that the rate of change of algal biomass over time is equal to the sum of the phosphorus-driven growth, diffusion, and convection terms minus the biomass loss term. Multiply the carbon-phosphorus co-emission spectral confusion potential field by the proportionality coefficients of the interfacial pulse release of dissolved inorganic phosphorus and the interfacial pulse release of colored soluble organic matter, respectively, to obtain the interfacial pulse release source terms of dissolved inorganic phosphorus and colored soluble organic matter.

7. A coupled simulation system for lake basin ecological processes based on data fusion according to claim 1, characterized in that, Define the state variable field, which includes dissolved inorganic phosphorus concentration, equivalent uptake intensity of colored dissolved organic matter, and algal biomass; Establish an observation operator. The first component of the observation operator is equal to the equivalent absorption intensity of colored soluble organic matter multiplied by the first linear coefficient plus the algal biomass multiplied by the second linear coefficient. The second component of the observation operator is equal to the equivalent absorption intensity of colored soluble organic matter multiplied by the third linear coefficient plus the algal biomass multiplied by the fourth linear coefficient.

8. A coupled simulation system for lake basin ecological processes based on data fusion according to claim 7, characterized in that, Construct an objective function, the first term of which is the observation fitting term. Calculate the difference vector between the output of the observation operator and the confusion-sensitive observation vector. Calculate the weighted norm square of the difference vector with respect to the observation error covariance matrix and integrate it over the spatiotemporal domain. The second term of the objective function is the spatial structure regularization term. The sum of the spatial gradient modulus square of the concentration of dissolved inorganic phosphorus and the spatial gradient modulus square of the equivalent absorption intensity of colored dissolved organic matter is calculated. The sum is multiplied by the confusion potential field of carbon-phosphorus co-emission spectrum and integrated over the spatiotemporal domain. Finally, the integral result is multiplied by the regularization intensity coefficient. Under the constraints of the state evolution equations, the optimal state variable field that minimizes the objective function is solved, and the fused dissolved inorganic phosphorus and fused algal biomass are extracted from the optimal state variable field.

9. A coupled simulation system for lake basin ecological processes based on data fusion according to claim 1, characterized in that, Obtain the fused dissolved inorganic phosphorus, calculate the product of the maximum specific growth coefficient and the fused dissolved inorganic phosphorus as the numerator of the monotonically saturated growth term, calculate the sum of the half-saturation constant and the fused dissolved inorganic phosphorus as the denominator of the monotonically saturated growth term, calculate the quotient of the numerator divided by the denominator, calculate the difference between the quotient and the loss coefficient, and set the difference as the net growth driving rate. Obtain the fused algal biomass, calculate the product of the fused algal biomass and the net growth driver rate, calculate the negative exponential function value of the product, calculate the sum of the positive and negative exponential function values, calculate the reciprocal of the sum, and set the reciprocal as the decongestion risk intensity of algal blooms.

10. A coupled simulation system for lake basin ecological processes based on data fusion according to claim 1, characterized in that, Obtain the deconfusion algal bloom risk intensity and the fused algal biomass, calculate the product of the fused algal biomass and the deconfusion algal bloom risk intensity, and set the product as the algal bloom exposure flux. Determine the start and end times of integration, perform time integration on the algal bloom exposure flux from the start to the end time, and set the result of the time integration as the cumulative exposure.

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