Reservoir dynamic management method based on three-dimensional GIS

Abstract

The invention relates to the technical field of reservoir water environment dynamic monitoring and intelligent prediction management, and discloses a reservoir dynamic management method based on a three-dimensional GIS. The method comprises the following steps: layering a water body from a water surface to a reservoir bottom in an equal-thickness manner, collecting turbidity and initial algae density layer by layer, obtaining a light receiving proportion and an incident illumination sequence of each layer in combination with three-dimensional geographic information, forming a corrected average turbidity and an illumination loss factor in a whole reservoir range, recurring residual illumination from top to bottom, and overlapping shielding and turbidity scattering to obtain net effective illumination of each layer; setting a light response threshold value according to the initial light field and the detection lower limit, calculating a light response factor in a layered manner, establishing a shallow preferential growth factor and a self-inhibition mechanism in combination with the relative depth, updating the algae density layer by layer under a fixed time step length, accumulating to obtain the algae amount per unit area and a risk index, and identifying a high-risk layer set and the high-risk water body thickness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reservoir water environment dynamic monitoring and intelligent prediction management, in particular to a reservoir dynamic management method based on three-dimensional GIS. BACKGROUND

[0002] With the rapid development of social economy and the improvement of water resource protection awareness, large reservoirs play an increasingly important role in ensuring urban water supply, flood control and dispatching, and ecological restoration. However, the outbreak of water bloom caused by abnormal growth of algae (such as blue-green algae) in reservoir water will seriously affect water quality safety and water ecological balance, and even cause drinking water crisis. Therefore, dynamic monitoring and accurate prediction of algae growth in reservoirs have become the core technical requirements of modern reservoir intelligent management and water environment protection.

[0003] In the prior art, the reservoir algae bloom monitoring and risk early warning method mainly relies on traditional water surface fixed-point sampling, regular water quality experimental analysis and two-dimensional remote sensing image monitoring. Some high-end applications introduce automatic monitoring buoys, conventional meteorological-hydrological monitoring systems and GIS spatial analysis platforms to perform data collection and display of the entire reservoir area. Although this method can reflect the overall water quality trend of the reservoir area, it can only provide algae concentration information at the water surface or a single depth point, and it is difficult to reflect the changes of light, turbidity and algae distribution at different water depth layers. At the same time, the existing prediction methods based on empirical formula or statistical model often assume that the water environment is uniform, lack three-dimensional layered dynamic modeling capability, and are difficult to depict the real influence of spatial structures such as topography, geomorphology and flow pattern on light attenuation and algae growth. In addition, the existing methods mostly use linear regression, threshold discrimination or mechanism models based on fixed parameters, which usually rely on preset weights, empirical coefficients or manual parameter tuning, lack adaptive coupling mechanism with actual environmental data, and are easily affected by environmental anomalies, extreme weather or invalidity of experience migration from other places. In the field of three-dimensional GIS reservoir management, although some documents have proposed three-dimensional terrain modeling and spatial attribute visualization, the dynamic coupling of three-dimensional spatial layering and environmental data, the quantitative dynamic deduction of the whole process of algae growth and the intelligent identification of high-risk layers are still technical problems in the industry. Especially in the case of complex topography, variable light, and heterogeneous turbidity, the existing models are difficult to realize complete closed-loop deduction from original environmental monitoring data to layered light, layered algae, and layered risk, and generally lack data-driven efficient computing methods for actual decision-making management.

[0004] To this end, the case aims to propose a reservoir dynamic management method based on three-dimensional GIS. First, the water layer is divided into equal-thickness layers from top to bottom, and basic data such as turbidity, algal density and light receiving ratio of each layer are obtained by combining water quality sensors and three-dimensional models to form a complete multi-dimensional time series data basis. Then, by constructing the transmission attenuation factor and the composite light intensity, the attenuation and scattering process of light in the water body is accurately simulated, and the net effective light input of each layer is calculated. Then, based on the initial net light and the minimum detectable light threshold, the light response growth factor is constructed, the priority growth factor affected by water depth is coupled with the self-inhibition term to form a multi-factor comprehensive growth model. Finally, the algal density is updated at a fixed time step, and the risk index and high-risk layer set are calculated in the whole reservoir layer to realize the quantitative early warning of algal bloom outbreak risk. SUMMARY

[0005] The present application provides a reservoir dynamic management method based on three-dimensional GIS, which solves the problems mentioned in the background art.

[0006] The present application provides the following technical scheme: a reservoir dynamic management method based on three-dimensional GIS, comprising:

[0007] Establishing vertical equal-thickness layers from the water surface to the maximum water depth, numbering each layer, collecting the average turbidity and initial algal density of each layer, and establishing the time record of the algal density of each layer, while obtaining the effective light receiving ratio and incident light intensity sequence of each layer given by the three-dimensional GIS model;

[0008] Forming the corrected average turbidity on the whole reservoir layer data, obtaining the relative turbidity and light loss factor of each layer, and recursively calculating the residual light intensity from top to bottom;

[0009] Integrating the effective light receiving ratio, obtaining the actual receivable light intensity of each layer, and introducing the turbidity scattering term to obtain the net effective light intensity of each layer;

[0010] Forming the light response threshold according to the net effective light intensity of each layer at the initial time and the minimum detectable light intensity, and calculating the light response growth factor of each layer;

[0011] Forming the shallow layer priority growth factor according to the relative depth, combining the fixed growth coefficient and the self-inhibition term;

[0012] Updating the algal density layer by layer at a fixed update time step and setting the zero lower limit;

[0013] Calculating the total amount of algal per unit area, constructing the reference algal amount and forming the risk index;

[0014] Generating the high-risk layer set according to the threshold criterion, counting the thickness of the high-risk water body and outputting each result.

[0015] Optionally, the step of establishing a vertically equal-thickness layer from the water surface to the maximum water depth, numbering each layer, collecting the average turbidity and initial algae density of each layer, and establishing a time record of algae density for each layer, while simultaneously obtaining the effective light-receiving ratio and incident light intensity sequence of each layer given by the 3D GIS model, specifically includes:

[0016] The water body of the reservoir is divided into multiple layers from the water surface to the maximum water depth according to the vertical equal thickness method, numbered sequentially, and the thickness of each layer is recorded.

[0017] The average turbidity and initial algae density in each layer were collected by water quality sensors, and a baseline record of algae density in each layer was established at the initial moment.

[0018] The density of algae in each layer is time-stamped and stored at any given time to form a continuous time series, while maintaining consistency between the initial time and the baseline;

[0019] In the 3D GIS model, the effective light-receiving ratio of each layer is calculated along the boundary between the water body and the terrain, with the value range set to zero to one.

[0020] The incident light intensity sequence was collected by meteorological equipment in a time series.

[0021] Optionally, the step of generating a corrected average turbidity on the full-database layer to obtain the relative turbidity and light loss factor of each layer, and recursively estimating the remaining light intensity from top to bottom, specifically includes:

[0022] Calculate the original average turbidity based on the average turbidity of each layer;

[0023] The minimum detectable limit of turbidity of the turbidity measuring device is read and compared with the original average turbidity to form a corrected average turbidity. The lower limit is controlled to be no lower than the minimum detectable limit of turbidity.

[0024] The relative turbidity of each layer is obtained by the ratio of the average turbidity of each layer to the corrected average turbidity, and the layer parameters are established for subsequent conversion.

[0025] The light loss factor for each layer is formed by combining the relative turbidity of each layer with the ratio of the layer thickness to the maximum water depth.

[0026] Starting from the uppermost layer unit at the water surface, the incident light intensity at the water surface is used as the starting value of the uppermost layer unit. The remaining light intensity is calculated recursively for each layer unit according to the vertical numbering order. For any layer unit, the calculation result of the adjacent layer unit directly above is set as the starting value of the current layer unit, until the bottom of the reservoir.

[0027] Optionally, the effective light-receiving ratio is incorporated to obtain the actual receiveable light intensity of each layer, and a turbidity scattering term is introduced to obtain the net effective light intensity of each layer, specifically including:

[0028] The effective light-receiving ratio of each layer is introduced into the remaining light intensity of the corresponding layer to obtain the actual light intensity that each layer can receive.

[0029] The net effective light intensity of each layer is obtained by adding the additional loss caused by turbidity scattering on the basis of relative turbidity and reducing the actual receiveable light intensity.

[0030] Optionally, the step of forming a light response threshold based on the net effective illuminance of each layer and the minimum detectable illuminance at the initial moment, and calculating the light response growth factor of each layer, specifically includes:

[0031] At the initial moment of the system, the net effective illuminance of each layer is averaged between layers to obtain the initial average net illuminance baseline.

[0032] The minimum detectable light intensity is provided by the illuminometer, and the light response threshold is set. The light response threshold is not lower than the minimum detectable light intensity.

[0033] The light response growth factor for each layer is generated based on the ratio of the current net effective light intensity to the light response threshold, and the light response growth factor is limited to the range of zero to one.

[0034] Optionally, the step of forming a shallow-layer preferential growth factor based on relative depth and combining a fixed growth coefficient with a self-inhibition term specifically includes:

[0035] The relative depth index of each layer is obtained based on the layer thickness and the maximum water depth.

[0036] Establish a shallow-layer priority growth factor based on relative depth, and set a decreasing relationship where the shallower the depth, the higher the weight.

[0037] By combining the light-responsive growth factor, the shallow-layer preferential growth factor, and the fixed growth coefficient, the contribution of algae growth to each layer can be obtained.

[0038] A density self-inhibition term is constructed based on the current algal density of each layer, and the self-inhibition scale is limited by the critical level of the entire library. The statistical results of the initial algal density of each layer are compared with the minimum detectable limit of algal density, and the critical level of the entire library is set according to the larger one.

[0039] Optionally, the step of updating algae density layer by layer with a fixed update time step and setting a zero lower limit specifically includes:

[0040] Set the update time step and calculate the temporary candidate algae density of each layer at any time. The calculation method is to add the difference between the growth and self-inhibition on the basis of the current density and multiply it by the time step to form the increment.

[0041] When the temporary candidate algae density is not less than zero, the temporary candidate algae density is written into the algae density of the corresponding layer at the next time step;

[0042] When the density of temporary candidate algae is less than zero, the algae density of the current layer at the next time step is set to zero.

[0043] Optionally, the calculation of the total algae content per unit area, the construction of a reference algae content, and the formation of a risk index specifically include:

[0044] The algae density of each layer is vertically summed according to the layer thickness to obtain the total amount of algae per unit area;

[0045] The initial reference algae content is obtained by averaging the total amount of algae per unit area at the initial moment between layers.

[0046] Based on the minimum detectable limit of algal density and the maximum water depth, a reference limit per unit area is constructed, and compared with the original reference algal quantity, and the larger one is taken as the reference algal quantity.

[0047] The dimensionless algal bloom risk index is constructed using the current total algae content per unit area, the initial total algae content per unit area, and the reference algae content.

[0048] Optionally, the step of generating a high-risk layer set based on threshold criteria, statistically analyzing the thickness of high-risk water bodies, and outputting various results specifically includes:

[0049] The criteria are set as follows: when the algal density of a certain layer exceeds the critical level of the entire pool, the layer that meets the criteria is marked as a high-risk layer, and the layers that meet the criteria are grouped into a set of high-risk layers.

[0050] When the set of high-risk layers is not empty, the thickness of each layer in the set of high-risk layers is summed to obtain the thickness of the high-risk water body; when the set of high-risk layers is empty, the thickness of the high-risk water body is set to zero.

[0051] Output the following results: current algal density sequence for all layers, set of high-risk layers, current total algal content per unit area, current risk index, and current thickness of high-risk water bodies.

[0052] The present invention has the following beneficial effects:

[0053] 1. By combining a 3D GIS model with a water quality sensor network, this method upgrades the traditional approach of single-point or surface sampling to full-reservoir stratified monitoring. It achieves simultaneous recording of layered data (equal thickness from the water surface to the reservoir bottom), including turbidity and initial algal density data for each layer. Furthermore, it includes the effective light-receiving ratio and incident light intensity sequence for each stratified unit within the GIS model. This approach accurately reflects the vertical heterogeneity of the water body, reducing errors caused by treating the entire water body as a homogeneous system. It provides a solid physical and spatiotemporal foundation for subsequent calculations of light attenuation and algal growth. Compared to existing technologies, this method eliminates the need for dense manual water sampling and expensive deep-water buoy arrays. It achieves high-precision full-reservoir stratified modeling using only conventional sensing equipment and a GIS analysis platform, reducing operational costs and improving monitoring coverage and data quality.

[0054] 2. Based on the data from the entire reservoir, a corrected average turbidity is generated, and the relative turbidity and light loss factor of each layer are calculated from this. This overcomes the limitations of traditional models that estimate light attenuation in water bodies using fixed coefficients. Real-time monitoring of turbidity is dynamically integrated into the light attenuation calculation, achieving a top-down, layer-by-layer recursive extraterrestrial light intensity calculation. It can accurately reflect the immediate impact of turbidity fluctuations caused by sudden environmental changes, heavy rain erosion, and sediment resuspension on light penetration, avoiding prediction errors caused by lag in attenuation parameters or deviations between model assumptions and actual conditions. Compared to existing technologies that set attenuation coefficients based on historical experience or experimental data, this scheme can calibrate the attenuation factor at any time, improving the model's response speed and accuracy to dynamic changes in water quality, and providing more reliable input for subsequent light distribution and algal response.

[0055] 3. Building upon the attenuation results of the previous layer, this method further applies the effective light-receiving ratio of the layered units to the remaining light intensity, and introduces an additional turbidity scattering loss term to obtain the truly usable net effective light intensity for algae. Simultaneously considering the dual effects of underwater topographic shading and water turbidity scattering, a more refined light environment framework is formed. This accurately characterizes the net light level when approaching eutrophication or during peak algae inoculation periods, avoiding biases caused by calculations based solely on attenuation or shading. Unlike traditional models that only consider shading or fixed scattering assumptions, this scheme adjusts scattering loss based on real-time turbidity weighting, making the simulation of algal photosynthesis closer to the real aquatic environment. This provides a breakthrough technical means for accurately assessing light availability and algal growth potential at different depths.

[0056] 4. The initial net effective light intensity of each layer and the minimum detectable light intensity given by the detection equipment are used to jointly determine the light response threshold. Based on this, the light response growth factor for each layer is dynamically calculated, ensuring that the factor value is distributed between zero and one to reflect the relative growth potential of algae under different light conditions. This overcomes the problem of traditional light response models requiring preset saturation or antagonistic parameters, and ensures that the potential response of algae is not underestimated under extremely low light conditions by using a detection lower limit. Compared with existing static threshold or single-parameter growth models, this scheme can adjust the response factor according to environmental changes, ensuring the adaptability and robustness of the water-light coupling model, making algal growth prediction more accurate, and thus providing more reliable monitoring of algal bloom trends for reservoir management.

[0057] 5. A shallow-layer preferential growth factor is formed based on the relative depth of each layer, and this factor is combined with a light-response growth factor and a pre-determined fixed growth coefficient. Simultaneously, a self-inhibition term based on algal density and the critical level of the entire pool is constructed, realizing the simulation of the dual superposition effect of algal growth and self-inhibition. For the first time, vertical depth advantage and biological self-inhibition mechanisms are simultaneously introduced into a stratified dynamic model; this effectively avoids overestimation of shallow, high-light areas and naturally inhibits growth under high-density conditions, making the simulation results more consistent with ecological principles. Compared to the single-factor growth or simplified inhibition methods in existing approaches, this scheme can more comprehensively reflect the dynamic balance between water depth and algal community constraints, improving prediction robustness and ecological rationality.

[0058] 6. Algae density is iteratively updated layer by layer with a fixed time step, and a zero lower bound is applied to truncate negative values, forming a simple yet efficient numerical integration framework. This approach balances computational efficiency with boundary condition handling: the time step mechanism ensures the capture of rapidly changing phases, while the zero lower bound constraint avoids numerical oscillations and non-physical results. It can perform real-time iterative calculations of large-scale hierarchical units under conventional hardware conditions, and the output results maintain physical interpretability. Compared with common techniques such as batch updates of the entire water body or solving complex differential equations, this iterative update method is simple to implement, easy to run online, and easily expandable.

[0059] 7. The total algae density per unit area is obtained by summing the density and thickness of each layer. A reference algae quantity is then constructed based on the average distribution of the initial total algae quantity and a minimum reference quantity, thus forming a dimensionless risk index, achieving risk quantification and standardization. The introduction of both initial and lower limit reference standards ensures the risk index reflects both absolute increments and relative comparisons; the risk levels have a unified scale, facilitating cross-temporal and spatial comparisons and threshold warnings. Unlike traditional methods that rely solely on a single density threshold or empirical level to determine risk, this scheme incorporates dynamic reference quantities in the risk assessment process, ensuring comparability of assessments across different seasons or reservoirs, and improving the scientific rigor and reliability of risk classification.

[0060] 8. Based on the comparison criteria between algal density and the critical level of the entire reservoir, a set of high-risk layers is dynamically generated and its overall water thickness is statistically analyzed. Finally, multi-dimensional results including layer density sequences, risk levels, and high-risk depths are output. This is the first time that high-risk assessment has been refined to the vertical layer level, achieving precise location of high-risk zones. It provides direct depth information for reservoir-based treatment measures such as stratified chemical and material application, stratified aeration, or stratified pollution interception, avoiding resource waste caused by blind overall treatment. Unlike existing technologies that only provide overall risk warnings or surface alarms, this solution guides managers in identifying and addressing algal bloom threats within specific depth ranges, improving the accuracy and effectiveness of emergency response and daily management. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Example, refer to Figure 1 A dynamic reservoir management method based on 3D GIS includes:

[0064] Establish vertical equal-thickness layers from the water surface to the maximum water depth, number each layer, collect the average turbidity and initial algae density of each layer, and establish a time record of algae density for each layer. At the same time, obtain the effective light-receiving ratio and incident light intensity sequence of each layer given by the 3D GIS model.

[0065] A corrected average turbidity is generated on the full-layer data, and the relative turbidity and light loss factor of each layer are obtained. The remaining light intensity is then deduced from top to bottom.

[0066] By incorporating the effective light-receiving ratio, the actual light intensity that each layer can receive is obtained, and by introducing the turbidity scattering term, the net effective light intensity of each layer is obtained.

[0067] The light response thresholds are formed based on the net effective illuminance and minimum detectable illuminance of each layer at the initial moment, and the light response growth factor of each layer is calculated.

[0068] Based on relative depth, a shallow-layer preferential growth factor is formed, and a fixed growth coefficient and a self-inhibition term are combined.

[0069] Algae density is updated layer by layer with a fixed update time step and a zero lower limit is set;

[0070] Calculate the total amount of algae per unit area, construct a reference algae content, and generate a risk index;

[0071] A set of high-risk layers is generated based on threshold criteria, the thickness of high-risk water bodies is statistically analyzed, and the results are output.

[0072] By first vertically stratifying the water body in the reservoir from the surface to the bottom with equal thickness, and collecting turbidity and initial algal density for each layer, and then combining this with 3D geographic information data to establish the effective light-receiving ratio and external illumination sequence for each layer, the problem of traditional one-dimensional or two-dimensional methods being unable to accurately characterize the vertical heterogeneity of the water body and the differences in illumination was solved. This provides a complete, continuous, and layered spatiotemporal data foundation for all subsequent calculations, avoiding errors caused by single-point or surface-level mixed measurements. Subsequently, dynamic illumination attenuation calculation based on real-time turbidity data and composite illumination intensity assessment considering topographic shading and turbidity scattering were introduced, making the description of the real illumination environment available for algal photosynthesis in each layer more accurate, effectively making up for the shortcomings of existing models that only provide static or semi-empirical estimations of illumination. Furthermore, by combining initial illumination conditions with the equipment's detection limit to establish a light response threshold and calculating response factors layer by layer, and by integrating this with depth-priority growth factors and biological self-inhibition mechanisms, the simulation process of algal growth is refined. This allows for a balance of the multiple effects of light advantage, depth advantage, and concentration inhibition when dynamically updating algal density. By accumulating the algal abundance of each layer to form a risk index and accurately identifying high-risk layers, managers can be provided with risk level, risk layer depth, and corresponding algal abundance data. This solves the problem that traditional methods can only provide overall or surface risk warnings without pinpointing the depth of layered risk. Overall, this invention, through integrated layered data acquisition, physical attenuation calculation, biological response simulation, and risk quantification output, achieves precise monitoring and early warning of algal bloom evolution across the entire reservoir layer, providing scientific and operable decision support for reservoir ecological management.

[0073] The process involves establishing vertical, uniformly thick strata from the water surface to the maximum water depth, numbering each layer, collecting the average turbidity and initial algal density of each layer, establishing a time record of algal density for each layer, and simultaneously obtaining the effective light-receiving ratio and incident light intensity sequence for each layer from the 3D GIS model. Specifically, this includes:

[0074] The water body of the reservoir is divided into multiple layers from the water surface to the maximum water depth according to the vertical equal thickness method, numbered sequentially, and the thickness of each layer is recorded.

[0075] The average turbidity and initial algae density in each layer were collected by water quality sensors, and a baseline record of algae density in each layer was established at the initial moment.

[0076] The density of algae in each layer is time-stamped and stored at any given time to form a continuous time series, while maintaining consistency between the initial time and the baseline;

[0077] In the 3D GIS model, the effective light-receiving ratio of each layer is calculated along the boundary between the water body and the terrain, with the value range set to zero to one.

[0078] The incident light intensity sequence was collected by meteorological equipment in a time series.

[0079] Further specific implementation steps include:

[0080] From the water surface to the maximum water depth of the reservoir Divided along the vertical direction into Layer unit, numbered as The thickness of each layer is ;in, This refers to the maximum water depth of the reservoir from the surface to the bottom. The total number of water body layer units obtained by dividing the water body in the vertical direction; Number the water body layers; For the first The thickness of the water layer unit;

[0081] In each of the Within the layer unit, the average turbidity of the water within the layer is obtained through water quality sensors. Initial algal density within the layer ;in, For the first The average turbidity of the water layer; For the first Algae density in the water layer at the initial moment;

[0082] At any time , will the The algae density in the layer of water is denoted as At the initial moment satisfy ;in, It is a continuous-time variable; This is the system initialization time;

[0083] Based on 3D The effective light-receiving ratio of each layer at the interface between the water body and the terrain is calculated in the model. ;in, For the first The effective proportion of external natural light received by a layer of water in the vertical direction, when When it means completely unobstructed, when This indicates complete occlusion;

[0084] Meteorological data collection equipment in continuous time The intensity of external light incident on the water surface at any given time is denoted as . .

[0085] By clearly defining and numbering water layers of equal thickness from the water surface to the reservoir bottom, and establishing unit thickness parameters, a clear coordinate system and physical boundaries are provided for subsequent layer-by-layer calculations. Subsequently, water quality sensors are used within each layer to simultaneously collect turbidity and initial algal density, establishing a time baseline. This combines static benchmarks with dynamic moments, overcoming the limitation of traditional methods that only sample at the beginning or random moments, failing to capture continuously changing trajectories. This approach not only ensures the consistency and comparability of data across layers but also overcomes the problem that two-dimensional maps or empirical shading coefficients cannot reflect actual topographic shading by calculating the effective light-receiving ratio of each layer in the 3D GIS model. Simultaneously, obtaining the incident light intensity sequence in a time series enables synchronous monitoring of natural light conditions, avoiding representativeness biases caused by using only meteorological station data. Compared to existing technologies, this scheme extends the data collection scope to the entire vertical dimension of the water body and integrates topographic effects and light variations, improving the completeness and reliability of subsequent model input data and laying a solid foundation for dynamic simulation.

[0086] The process of generating a corrected average turbidity from the full-layer data, obtaining the relative turbidity and light loss factor for each layer, and recursively estimating the remaining light intensity from top to bottom, specifically includes:

[0087] Calculate the original average turbidity based on the average turbidity of each layer;

[0088] The minimum detectable limit of turbidity of the turbidity measuring device is read and compared with the original average turbidity to form a corrected average turbidity. The lower limit is controlled to be no lower than the minimum detectable limit of turbidity.

[0089] The relative turbidity of each layer is obtained by the ratio of the average turbidity of each layer to the corrected average turbidity, and the layer parameters are established for subsequent conversion.

[0090] The light loss factor for each layer is formed by combining the relative turbidity of each layer with the ratio of the layer thickness to the maximum water depth.

[0091] Starting from the uppermost layer unit at the water surface, the incident light intensity at the water surface is used as the starting value of the uppermost layer unit. The remaining light intensity is calculated recursively for each layer unit according to the vertical numbering order. For any layer unit, the calculation result of the adjacent layer unit directly above is set as the starting value of the current layer unit, until the bottom of the reservoir.

[0092] Further specific implementation steps include:

[0093] Calculate the raw average turbidity obtained by averaging the turbidity of each layer. ;

[0094] Obtain the minimum detectable turbidity limit given by the turbidity measurement device, denoted as . ;

[0095] based on and The corrected average turbidity was obtained. Specifically:

[0096] ;

[0097] Calculate the relative turbidity of each layer: ;in, For the first The relative turbidity of the water layer unit;

[0098] For the Layered water units, constructing light loss factors Specifically:

[0099] ;

[0100] The intensity of transmitted light is calculated layer by layer from the water surface downwards. The initial conditions are: The recursive relation is , ;in, As light passes through the upper layers of water in sequence from the surface to reach the first layer... The remaining light intensity at the top interface; This represents the remaining light intensity at the water surface.

[0101] First, a corrected average turbidity is obtained by comparing the average turbidity with the minimum detectable limit, avoiding negative values ​​due to excessively low turbidity or distortion due to excessively high turbidity. Then, the light loss factor is derived from this corrected turbidity and the ratio of each layer thickness, ensuring that the light attenuation of each layer reflects the current water quality state rather than a fixed empirical value. This solves the problem of existing technologies assuming overly static light attenuation parameters that are out of touch with actual water quality. Based on this, a top-down light intensity recursion is adopted, using the incident light intensity at the water surface as the starting point for the top layer and sequentially transmitting it to each layer unit. The calculation result of the previous layer unit is explicitly designated as the starting value for the next layer, achieving traceability of the light intensity transmission logic and inter-layer connection. This addresses the deficiency of traditional models that neglect inter-layer continuity, ensuring the consistency and accuracy of the entire water attenuation process.

[0102] The effective light-receiving ratio is incorporated to obtain the actual receiveable light intensity of each layer, and a turbidity scattering term is introduced to obtain the net effective light intensity of each layer. Specifically, this includes:

[0103] The effective light-receiving ratio of each layer is introduced into the remaining light intensity of the corresponding layer to obtain the actual light intensity that each layer can receive.

[0104] The net effective light intensity of each layer is obtained by adding the additional loss caused by turbidity scattering on the basis of relative turbidity and reducing the actual receiveable light intensity.

[0105] By incorporating the shading ratio of each layer, the actual receiveable light intensity is calculated as follows:

[0106] ;in, In the first Layer in time The actual light intensity received after terrain obstruction is always taken into account;

[0107] In relative turbidity Based on this, scattering loss caused by turbidity is introduced to construct the effective light intensity of this layer that can ultimately be used for algal photosynthesis, specifically:

[0108] ;in, For the first Layer in time At that moment, after being blocked and turbidity scattered, is the net effective light intensity that can actually be used by algae for photosynthesis.

[0109] By combining the aforementioned residual light intensity with the effective light-receiving ratio of each layer, the actual receiveable light intensity of each layer is obtained, truly reflecting the spatial differences caused by underwater topography and vegetation obstruction. Subsequently, a turbidity-based scattering loss is introduced to further reduce the actual receiveable light intensity, resulting in net effective light that can be utilized by algae. This series of steps, by simultaneously considering the dual effects of topographic obstruction and water turbidity scattering, solves the model bias problem caused by focusing on only a single factor in existing technologies. Specifically, when the turbidity of the water body fluctuates significantly due to wind and waves, the scattering loss term in this method can adjust the net light estimation in real time, avoiding overly optimistic or pessimistic assessments of algal photosynthetic potential; and when there is localized shade from trees or buildings on the water surface, the effective light-receiving ratio term ensures accurate representation of localized light shading.

[0110] The process involves establishing light response thresholds based on the initial net effective illuminance and minimum detectable illuminance of each layer, and calculating the light response growth factor for each layer. Specifically, this includes:

[0111] At the initial moment of the system, the net effective illuminance of each layer is averaged between layers to obtain the initial average net illuminance baseline.

[0112] The minimum detectable light intensity is provided by the illuminometer, and the light response threshold is set. The light response threshold is not lower than the minimum detectable light intensity.

[0113] The light response growth factor for each layer is generated based on the ratio of the current net effective light intensity to the light response threshold, and the light response growth factor is limited to the range of zero to one.

[0114] Further specific implementation steps include:

[0115] First, calculate the average net illumination of each layer at the initial moment of the system:

[0116] ;in, For the initial moment Below, the average net light intensity of each layer in the entire reservoir area;

[0117] Combined with the minimum detectable light intensity of the illuminometer Construct the photoresponse threshold, specifically as follows:

[0118] ;in, The light intensity threshold for the photoresponse of algae;

[0119] Construct the first Layer at time The light response growth factor is specifically:

[0120] ;in, In time At that moment, the The relative growth response coefficient of strata algae under light conditions.

[0121] By combining the average net light level at the initial moment of the system with the instrument's minimum detectable light intensity, the response threshold is determined. This ensures that the threshold is both higher than the minimum detection range and not lower than the initial environmental level, thus avoiding false positives caused by setting the response threshold too low or false negatives caused by setting it too high. Subsequently, a light response growth factor is generated based on the ratio of the current net effective light level of each layer to this dynamic threshold, distributing the factor between zero and one to fully reflect the differences in algal photosynthetic potential under different light conditions. This step innovatively constructs an adaptive light response model, solving the problem that traditional static or empirical threshold-based algal response models cannot automatically adjust to environmental changes. During periods of drastic changes in cyanobacteria distribution, this model can correct the response sensitivity in real time, avoiding growth prediction errors caused by threshold lag, thereby improving the accuracy of early warnings for the initial stages of algal blooms.

[0122] The process of forming a shallow-layer preferential growth factor based on relative depth, and combining a fixed growth coefficient with a self-inhibition term, specifically includes:

[0123] The relative depth index of each layer is obtained based on the layer thickness and the maximum water depth.

[0124] Establish a shallow-layer priority growth factor based on relative depth, and set a decreasing relationship where the shallower the depth, the higher the weight.

[0125] By combining the light-responsive growth factor, the shallow-layer preferential growth factor, and the fixed growth coefficient, the contribution of algae growth to each layer can be obtained.

[0126] A density self-inhibition term is constructed based on the current algal density of each layer, and the self-inhibition scale is limited by the critical level of the entire library. The statistical results of the initial algal density of each layer are compared with the minimum detectable limit of algal density, and the critical level of the entire library is set according to the larger one.

[0127] Further specific implementation steps include:

[0128] First calculate the... Relative depth of layers ;

[0129] Based on relative depth, a shallow-layer priority growth factor is constructed, specifically as follows: ;in, For the first Shallow layer preferential growth factor;

[0130] Construct the algal growth rate function as follows: ;in, In time At that moment, the The contribution of the growth of strata algae to the combined effects of light and water depth;

[0131] Construct an algal density inhibition term, specifically as follows: ;in, In time At that moment, the The self-inhibition caused by the excessively high algae density in the layer; This represents the critical level for algal density across the entire reservoir area. ; This is the minimum detectable limit for algae density detection equipment.

[0132] By establishing a shallow-layer preferential growth factor based on relative depth, the influence of water depth on algal light exposure is quantified into a coefficient, solving the problem of traditional models that only consider depth effects externally and are difficult to couple with other factors. Simultaneously, the aforementioned light-response growth factor, shallow-layer preferential factor, and fixed growth coefficient are combined to form a comprehensive growth rate, allowing the model to reflect both the coupling effect of light and depth and retain the experimentally or empirically determined maximum growth potential of organisms. Furthermore, a self-inhibition term based on algal density and critical levels is constructed to automatically suppress the growth rate as algal density increases, avoiding overfitting of the initial rapid growth phase while neglecting the resource competition and self-limiting effects caused by excessive density in the later stages.

[0133] The method of updating algae density layer by layer with a fixed update time step and setting a zero lower limit specifically includes:

[0134] Set the update time step and calculate the temporary candidate algae density of each layer at any time. The calculation method is to add the difference between the growth and self-inhibition on the basis of the current density and multiply it by the time step to form the increment.

[0135] When the temporary candidate algae density is not less than zero, the temporary candidate algae density is written into the algae density of the corresponding layer at the next time step;

[0136] When the density of temporary candidate algae is less than zero, the algae density of the current layer at the next time step is set to zero.

[0137] Further specific implementation steps include:

[0138] Given any time To update the time step Perform layer density updates, specifically as follows:

[0139] S601. First, calculate the density of temporary candidate algae. Specifically:

[0140] ;

[0141] S602, if Then update to ;

[0142] S603, if Therefore, the lower limit for algae density is set to 0. .

[0143] An iterative update method with a fixed time step is adopted. The difference between the comprehensive growth and self-inhibition is multiplied by the time increment and summed to the current algal density, with negative results truncated. This approach achieves numerical integration of algal density evolution at each layer through simple incremental operations and boundary constraints, ensuring computational efficiency while avoiding numerical oscillations and non-physical results that may occur in solving ordinary differential equations. This step solves the problems of high computational cost and poor algorithm stability caused by using complex differential equations or batch updates in existing technologies, while retaining necessary ecological constraints to keep the simulation results within a reasonable range.

[0144] The calculation of the total algae content per unit area, the construction of a reference algae content, and the formation of a risk index specifically include:

[0145] The algae density of each layer is vertically summed according to the layer thickness to obtain the total amount of algae per unit area;

[0146] The initial reference algae content is obtained by averaging the total amount of algae per unit area at the initial moment between layers.

[0147] Based on the minimum detectable limit of algal density and the maximum water depth, a reference limit per unit area is constructed, and compared with the original reference algal quantity, and the larger one is taken as the reference algal quantity.

[0148] The dimensionless algal bloom risk index is constructed using the current total algae content per unit area, the initial total algae content per unit area, and the reference algae content.

[0149] Further specific implementation steps include:

[0150] Calculate the total amount of algae per unit area of ​​the reservoir: ;in, In time At any given moment, the total amount of algae accumulated per unit area in the vertical direction of the entire reservoir;

[0151] Construct an average layer distribution based on the initial reference algal mass at the initial time point and the initial total algal mass:

[0152] ;in, For the initial moment The total amount of algae per unit area; The original reference areal density is obtained by evenly distributing the initial total algal content to each layer;

[0153] Combined with the lower limit of algae density detection With maximum water depth Lower limit of surface density:

[0154] ;in, Based on the minimum density lower limit With maximum water depth The derived minimum reference algae content per unit area;

[0155] Structural reference algae content ;

[0156] And construct the current algal bloom risk level index, specifically: ;in, In time Algal bloom risk level index at any given time.

[0157] The total algae content per unit area is calculated by summing the algae density and thickness of each layer. A dual reference area is then constructed using both the initial average distribution and the minimum detection limit. The larger value is used as the denominator for risk calculation, ultimately generating a dimensionless risk index. This index reflects both absolute algae content changes and provides relative comparative significance. This approach overcomes the limitations of existing single threshold or empirical classifications, which make horizontal comparisons difficult. It ensures the risk index maintains a consistent standard across different time points and water conditions, facilitating cross-period, cross-regional, and cross-project comparative analysis and alerts. The dual reference values ​​ensure the risk index remains within a reasonable range even when initial algae content is extremely low or the detection limit is increased, preventing index distortion.

[0158] The process of generating a high-risk layer set based on threshold criteria, calculating the thickness of high-risk water bodies, and outputting various results specifically includes:

[0159] The criteria are set as follows: when the algal density of a certain layer exceeds the critical level of the entire pool, the layer that meets the criteria is marked as a high-risk layer, and the layers that meet the criteria are grouped into a set of high-risk layers.

[0160] When the set of high-risk layers is not empty, the thickness of each layer in the set of high-risk layers is summed to obtain the thickness of the high-risk water body; when the set of high-risk layers is empty, the thickness of the high-risk water body is set to zero.

[0161] Output the following results: current algal density sequence for all layers, set of high-risk layers, current total algal content per unit area, current risk index, and current thickness of high-risk water bodies.

[0162] Further specific implementation steps include:

[0163] Judge the first Whether a layer is a high-risk layer for algal blooms is determined by the following criteria: The set consists of all layers that satisfy the criterion. ;in, In time The set of layer numbers that constitute a high-risk layer at any given moment;

[0164] The total thickness of high-risk water bodies is calculated as follows:

[0165] S801, when When not empty, let ;in, In time The total thickness of the water body at any given moment, composed of all high-risk layers;

[0166] S802, when When it is an empty set, let ;

[0167] Output the following data: Current algae density in all layers. The current high-risk layer set Current overall algal population Current risk level The current thickness of high-risk water bodies .

[0168] By comparing the algal density of each layer with the critical level, all layers exceeding the limit are dynamically grouped into a high-risk layer set, and their cumulative water thickness is calculated. Finally, multi-dimensional information including the stratified algal density sequence, the depth range of the high-risk layer, the overall algal quantity, and the risk level is output. This process solves the problem of existing technologies that can only provide the overall reservoir risk probability or single-point risk warnings but cannot specify the specific depth range. Through the high-risk layer set and thickness statistics, managers can accurately perform stratified drug administration, stratified aeration, or targeted algae inhibitor application, avoiding the resource waste and secondary pollution of traditional comprehensive treatment methods. Furthermore, the output data supports visualization and dynamic tracking, making emergency response more timely and efficient, greatly improving the precision and cost-effectiveness of reservoir management.

[0169] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0170] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A reservoir dynamic management method based on three-dimensional GIS, characterized in that, The method comprises the following steps: establishing vertical equal-thickness layers from the water surface to the maximum water depth, numbering each layer, collecting the average turbidity and initial algae density of each layer, and establishing a time record of the algae density of each layer, while obtaining the effective light receiving proportion and incident light intensity sequence of each layer given by the three-dimensional GIS model; forming a corrected average turbidity on the full reservoir layer data, obtaining the relative turbidity and light loss factor of each layer, and recursively calculating the residual light intensity from top to bottom; integrating the effective light receiving proportion to obtain the actual receivable light intensity of each layer, and introducing the turbidity scattering term to obtain the net effective light intensity of each layer; forming a light response threshold according to the net effective light intensity of each layer at the initial moment and the minimum detectable light intensity, and calculating the light response growth factor of each layer; forming a shallow layer priority growth factor according to the relative depth, combining the fixed growth coefficient and the self-inhibition term; updating the algae density layer by layer with a fixed update time step and setting a zero lower limit; calculating the total algae amount per unit area, constructing a reference algae amount, and forming a risk index; generating a high-risk layer set according to the threshold criterion, calculating the thickness of the high-risk water body, and outputting each result.

2. The reservoir dynamic management method based on three-dimensional GIS according to claim 1, characterized in that, The method comprises the following steps: dividing the reservoir water body into multiple layers from the water surface to the maximum water depth in a vertical equal-thickness manner, numbering them in turn, and recording the thickness of each layer; collecting the average turbidity and initial algae density in each layer through a water quality sensor, and establishing a baseline record of the algae density of each layer at the initial moment; time-tagging and storing the algae density of each layer at any moment to form a continuous time sequence, and keeping the baseline consistent at the initial moment; calculating the effective light receiving proportion of each layer along the water body and terrain interface in the three-dimensional GIS model, with a value range of zero to one; collecting the incident light intensity sequence in time sequence by a meteorological device.

3. The reservoir dynamic management method based on three-dimensional GIS according to claim 2, characterized in that, The method comprises the following steps: calculating the original average turbidity according to the average turbidity of each layer; reading the turbidity minimum detectable lower limit of the turbidity measuring device, and forming the corrected average turbidity after comparison with the original average turbidity, with the lower limit controlled not to be lower than the turbidity minimum detectable lower limit; obtaining the relative turbidity of each layer from the ratio of the average turbidity to the corrected average turbidity, and establishing layer parameters for subsequent conversion; combining the relative turbidity of each layer with the proportion of layer thickness to the maximum water depth to form the light loss factor of each layer; starting from the uppermost layer unit from the water surface, taking the incident light intensity at the water surface as the starting value of the uppermost layer unit, and recursively calculating the residual light intensity of each layer unit in the vertical order; for any layer unit, taking the calculation result of the adjacent layer unit above as the starting value of the current layer unit, until the bottom of the reservoir.

4. The reservoir dynamic management method based on three-dimensional GIS according to claim 3, characterized in that, ​ Introduce the effective light receiving proportion of each layer to the residual light intensity of the corresponding layer to obtain the actual receivable light intensity of each layer; On the basis of relative turbidity, superimpose the additional loss caused by turbidity scattering, and reduce the actual receivable light intensity to obtain the net effective light intensity of each layer.

5. The reservoir dynamic management method based on three-dimensional GIS according to claim 4, characterized in that, Form a light response threshold according to the net effective light intensity of each layer at the initial moment and the minimum detectable light intensity, and calculate the light response growth factor of each layer, specifically including: Interlayer average the net effective light intensity of each layer at the initial moment of the system to obtain the initial average net light reference; Provide the minimum detectable light intensity from the light meter, set the light response threshold, and the light response threshold is not lower than the minimum detectable light intensity; Generate the light response growth factor of each layer according to the ratio of the current net effective light intensity of each layer to the light response threshold, and limit the light response growth factor to the interval of zero to one.

6. The reservoir dynamic management method based on three-dimensional GIS according to claim 5, characterized in that, Form a shallow layer priority growth factor according to the relative depth, and combine a fixed growth coefficient and a self-inhibition term, specifically including: Obtain the relative depth index of each layer according to the layer thickness and the maximum water depth; Establish a shallow layer priority growth factor according to the relative depth, and set a decreasing relationship that the shallower the depth, the higher the weight; Combine the light response growth factor, the shallow layer priority growth factor, and the fixed growth coefficient to obtain the contribution of the algal growth of each layer; Construct a density self-inhibition term with the current algal density of each layer, and limit the self-inhibition scale with the critical level of the whole reservoir; compare the statistical results of the initial algal density of each layer with the minimum detectable lower limit of algal density, and set the critical level of the whole reservoir according to the larger one.

7. The reservoir dynamic management method based on three-dimensional GIS according to claim 6, characterized in that, Update the algal density layer by layer with a fixed update time step, and set a zero lower limit, specifically including: Set the update time step, calculate the selected algal density of each layer at any moment, and the calculation method is to add the increment formed by the difference between the growth amount and the self-inhibition amount multiplied by the time step to the current density; When the selected algal density at the critical moment is not less than zero, write the selected algal density at the critical moment into the algal density of the corresponding layer at the next moment; When the selected algal density at the critical moment is less than zero, set the algal density of the current layer at the next moment to zero.

8. The reservoir dynamic management method based on three-dimensional GIS according to claim 7, characterized in that, Calculate the total algal amount per unit area, construct a reference algal amount, and form a risk index, specifically including: Vertically accumulate the algal density of each layer according to the layer thickness to obtain the total algal amount per unit area; Interlayer average the total algal amount per unit area at the initial moment to form the original reference algal amount; Construct a lower limit reference amount per unit area according to the minimum detectable lower limit of algal density and the maximum water depth, and compare it with the original reference algal amount, and take the larger one as the reference algal amount; Construct a dimensionless algal bloom risk index with the current total algal amount per unit area, the initial total algal amount per unit area, and the reference algal amount.

9. The reservoir dynamic management method based on three-dimensional GIS according to claim 8, characterized in that, Generate a high-risk layer set according to the threshold criterion, count the thickness of the high-risk water body, and output each result, specifically including: Set the criterion: when the algal density of a layer exceeds the critical level of the whole reservoir, mark the layer that meets the condition as a high-risk layer, and form a high-risk layer set with the layers that meet the condition; When the high-risk layer set is not empty, sum the layer thickness of each layer in the high-risk layer set to obtain the thickness of the high-risk water body; when the high-risk layer set is empty, set the thickness of the high-risk water body to zero. The following results are output: the current algal density sequence of all layers, the set of high-risk layers, the current total amount of algae per unit area, the current risk index, and the current thickness of the high-risk water body.

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