Algal bloom control methods, devices, electronic equipment, media and process products

Abstract

This application relates to the field of water ecological governance technology, and discloses a method, device, electronic equipment, medium, and program product for controlling algal blooms. The method includes: acquiring algal bloom-related data for a monitored water area; the algal bloom-related data includes: satellite remote sensing data, meteorological data, and device parameters of an algae removal device; determining a water temperature prediction curve based on the meteorological data; determining a deployment scheme for the algae removal device based on the satellite remote sensing data, the water temperature prediction curve, and the device parameters, with the goal of minimizing the total governance cost; determining the operating parameters of the algae removal device based on the deployment scheme, with the goal of minimizing total energy consumption; controlling the operation of the algae removal device according to the operating parameters; acquiring the real-time water temperature of the monitored water area during the operation of the algae removal device; and adjusting the operating parameters of the algae removal device according to the real-time water temperature. This application can improve the effectiveness of algal bloom control.

Description

Technical Field

[0001] This application relates to the field of water ecological governance technology, specifically to methods, devices, electronic equipment, media, and program products for controlling algal blooms. Background Technology

[0002] Large lakes and reservoirs, as well as other still-water environments, are prone to algal blooms, which disrupt the ecological balance of the water body and reduce water transparency. Therefore, efficient algal bloom control has become a key requirement for aquatic ecological environment management. Related technologies involve deploying algae removal devices such as ultrasonic devices and dredging equipment. However, these technologies only indicate that algae removal devices can be deployed in the algal bloom area, without specifying how to deploy them for rapid algae removal and algal bloom control. This results in low resource allocation efficiency and poor overall control effectiveness. Summary of the Invention

[0003] This application provides a method, apparatus, electronic equipment, medium, and program product for controlling algal blooms, in order to solve the problems of traditional solutions that make it difficult to rationally deploy algae removal devices, resulting in low efficiency in the allocation of control resources and poor algal bloom control effects.

[0004] Firstly, this application provides a method for controlling algal blooms, the method comprising:

[0005] Acquire data related to algal blooms in the monitored water area; the algal bloom-related data includes: satellite remote sensing data, meteorological data, and device parameters of the algae removal device. A water temperature prediction curve is determined based on the meteorological data; Based on the satellite remote sensing data, the water temperature prediction curve, and the device parameters, a deployment scheme for the algae removal device is determined with the goal of minimizing the total treatment cost. Based on the deployment scheme of the algae removal device, the operating parameters of the algae removal device are determined with the goal of minimizing total energy consumption; The operation of the algae removal device is controlled according to its operating parameters. During the operation of the algae removal device, the real-time water temperature of the monitored water area is acquired; The operating parameters of the algae removal device are adjusted according to the real-time water temperature.

[0006] The method provided in this application constructs a comprehensive governance system, encompassing deployment plan formulation, operational parameter optimization, and real-time control, by collaboratively utilizing satellite remote sensing data, meteorological data, and device parameters of algae removal equipment. First, based on multi-source data, a deployment plan for the algae removal equipment is determined with the goal of minimizing total governance costs, solving the problem of blindly deploying equipment in traditional methods. Then, operational parameters are optimized with the goal of minimizing total energy consumption, ensuring the accuracy of governance. Finally, dynamic adjustment of control parameters through real-time water temperature achieves flexibility in the governance process. This method effectively integrates the requirements of economy, accuracy, and real-time performance, avoids waste of governance resources, significantly improves resource allocation efficiency and algae bloom control effects, and successfully solves the technical problems of traditional methods, such as the difficulty in rationally deploying algae removal equipment and poor governance results.

[0007] In one possible implementation, determining the deployment scheme of the algae removal device based on the satellite remote sensing data, the water temperature prediction curve, and the device parameters, with the objective of minimizing the total treatment cost, includes: A first model is pre-constructed; the first model includes a first objective function and a first constraint; the first objective function is constructed with the goal of minimizing the total governance cost, which includes: the operating cost of the algae removal device and the penalty cost for delays in governance progress; The first constraint includes: the quantity constraint of algae removal devices, the setting constraint of algae removal devices, and the cost ceiling constraint; Based on the satellite remote sensing data, the water temperature prediction curve, and the device parameters, the first model is solved to obtain the deployment scheme of the algae removal device.

[0008] The method provided in this application constructs a first model containing a first objective function and first constraints, minimizing the total treatment cost as the core objective, while also considering multiple constraints such as the number of devices, their density, and cost limits. The first objective function encompasses the operating cost of the algae removal devices and the penalty cost for treatment delays, ensuring comprehensive cost control; the various constraints are based on actual treatment needs, ensuring the feasibility and effectiveness of the deployment plan. By inputting satellite remote sensing data, water temperature prediction curves, and device parameters to solve the model, the formulation of the deployment plan has solid data support and rigorous logical derivation, avoiding biases caused by subjective decisions, achieving scientific and optimized deployment of algae removal devices, further improving the efficiency of treatment resource allocation, and laying the foundation for subsequent efficient treatment.

[0009] In one possible implementation, the constraints for setting up the algae removal device include: the installation density of the algae removal device in the high-density area is greater than or equal to a first preset value; the algae density in the high-density area is greater than or equal to a preset density value; the algae density is obtained based on an algae aggregation distribution map; the algae aggregation distribution map includes the distribution and concentration variation patterns of algae in the monitored water area within a future preset time period.

[0010] The method provided in this application achieves precise focusing of algae removal device deployment by clearly defining the density constraints for algae removal devices in high-density areas. Based on an algae aggregation distribution map containing the distribution and concentration changes of algae over a preset future time period, this method identifies high-density areas where algae density is greater than or equal to a preset density value. It requires that the density of algae removal devices in these areas be no less than a first preset value, ensuring that treatment resources are tilted towards key areas. This differentiated density constraint strategy avoids the problems of insufficient treatment in key areas and wasted resources in non-key areas caused by traditional uniform device deployment. It enables efficient use of limited treatment resources, significantly improves the treatment efficiency and overall effect in high-density areas, and further optimizes the rationality of treatment resource allocation.

[0011] In one possible implementation, the deployment scheme based on the algae removal device determines the operating parameters of the algae removal device with the objective of minimizing total energy consumption, including: A second model is pre-constructed; the second model includes a second objective function and second constraints; the second objective function is constructed with the goal of minimizing total energy consumption. The second set of constraints includes: processing capacity constraints and governance effectiveness constraints; The second model is solved based on the deployment scheme of the algae removal device to obtain the operating parameters of the algae removal device.

[0012] The method provided in this application constructs a second model with the goal of minimizing total energy consumption, while simultaneously applying constraints to ensure the accuracy and timeliness of the treatment. This effectively improves the accuracy and response efficiency of algal bloom control, providing a strong guarantee for achieving rapid and efficient treatment.

[0013] In one possible implementation, adjusting the operating parameters of the algae removal device based on the real-time water temperature includes: Calculate the water temperature deviation based on the real-time water temperature and the preset target water temperature; The operating parameters are adjusted based on the water temperature deviation.

[0014] The method provided in this application calculates the water temperature deviation between the real-time water temperature and the preset target water temperature, and adjusts the operating parameters according to the water temperature deviation. This ensures that the control of the algae removal device always revolves around the core need to inhibit algal bloom growth, can dynamically adapt to changes in the aquatic environment, and ensures that the water temperature remains stable in a range conducive to inhibiting algal blooms. This significantly improves the flexibility and adaptability of the treatment process and effectively addresses the challenges of dynamic environmental changes in algal bloom control.

[0015] In one possible implementation, after controlling the operation of the algae removal device according to its operating parameters, the method further includes: Acquire monitoring indicators for the monitored water area; the monitoring indicators include algal bloom area, real-time water temperature, and operational status data of the algae removal device; Determine whether the preset acceptance criteria are met based on the monitoring indicators.

[0016] The method provided in this application establishes a closed-loop verification system for the treatment effect by acquiring monitoring indicators such as algal bloom area, real-time water temperature, and device operating status data after the device is in operation, and evaluating them against preset acceptance standards. The monitoring indicators comprehensively cover the treatment effect, environmental status, and equipment operation, reflecting the effectiveness of the treatment work in all aspects. The preset acceptance standards provide a clear basis for judging the treatment effect, ensuring the objectivity and consistency of the evaluation. Through this verification process, problems in the treatment process can be identified in a timely manner. If the acceptance standards are not met, the deployment plan, operating parameters, or real-time control strategies can be adjusted accordingly to achieve continuous optimization of the treatment process. If the standards are met, the effectiveness of the treatment plan is confirmed, providing a reference for subsequent routine treatment. This closed-loop verification mechanism further guarantees the effectiveness of algal bloom treatment, ensuring that the treatment work achieves the expected goals.

[0017] Secondly, this application provides an algal bloom control device, the device comprising: The first processing module is used to acquire algal bloom-related data of the monitored water area; the algal bloom-related data includes: satellite remote sensing data, meteorological data, and device parameters of the algae removal device. The second processing module is used to determine the water temperature prediction curve based on the meteorological data; The third processing module is used to determine the deployment scheme of the algae removal device based on the satellite remote sensing data, the water temperature prediction curve and the device parameters, with the goal of minimizing the total treatment cost. The fourth processing module is used to determine the operating parameters of the algae removal device based on the deployment plan of the algae removal device with the goal of minimizing total energy consumption; The fifth processing module is used to control the operation of the algae removal device according to its operating parameters; The sixth processing module is used to acquire the real-time water temperature of the monitored water area during the operation of the algae removal device; The seventh processing module is used to adjust the operating parameters of the algae removal device according to the real-time water temperature.

[0018] Thirdly, this application provides an electronic device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the algal bloom control method described in the first aspect or any corresponding embodiment.

[0019] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions for causing a computer to execute the algal bloom control method described in the first aspect or any of its corresponding optional embodiments.

[0020] Fifthly, embodiments of this application provide a computer program product, which includes computer instructions for causing a computer to execute the algal bloom control method described in the first aspect or any of its corresponding optional embodiments. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram illustrating an application scenario according to an embodiment of this application; Figure 2 This is a flowchart of an algal bloom control method according to an embodiment of this application; Figure 3 This is a structural block diagram of an algal bloom control device according to an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of this application, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this application in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0025] As one optional application scenario in the embodiments of this application, such as Figure 1 As shown, the algal bloom development trend prediction system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0026] The terminal device can be a smartphone, tablet, laptop, PDA, or desktop computer. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranets, local area networks, wide area networks, mobile communication networks, and combinations thereof.

[0027] According to an embodiment of this application, an embodiment of a method for controlling algal blooms is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] This embodiment provides a method for controlling algal blooms. Figure 2 This is a flowchart of an algal bloom control method according to an embodiment of this application, such as... Figure 2 As shown, the process includes the following steps: S201: Obtain data related to algal blooms in the monitored water area.

[0029] In this embodiment of the application, the algal bloom-related data includes: satellite remote sensing data, meteorological data, and device parameters of the algae removal device.

[0030] In this embodiment, the satellite remote sensing data includes panchromatic remote sensing images of the monitored water area acquired by a satellite equipped with a multispectral remote sensing sensor. The image size is 1024×1024 pixels, with a spatial resolution of 2 meters, and the acquisition bands include blue-green, red, green, and near-infrared bands. As an example, satellite remote sensing data can be acquired through geostationary orbit satellites, and remote sensing images of the monitored water area can be acquired as the satellite passes overhead, enabling large-scale, periodic monitoring of algal bloom areas.

[0031] Meteorological data includes water temperature, air temperature, light intensity, wind speed, wind direction, precipitation, air pressure, and solar radiation intensity, as well as forecasts of the above meteorological parameters for a predetermined future time period, such as 24 hours, provided by the meteorological forecasting center. As an example, meteorological data can be obtained by monitoring automatic weather stations deployed along the waterways and connecting to a networked meteorological forecasting center. This allows for the synchronous acquisition of meteorological data and satellite remote sensing images at the time of their transit, ensuring consistent meteorological monitoring conditions for each remote sensing pixel.

[0032] The device parameters for algae removal equipment include device type (ultrasonic algae removal device, retrieval algae removal device), daily operating area per unit, operating cost function parameters, energy consumption per unit flow rate, purchase cost per unit, and effective radius of action. It also includes specific performance parameters for different types of algae removal devices (such as the operating frequency of ultrasonic algae removal devices and the operating efficiency of retrieval algae removal devices). As an example, device parameters for algae removal equipment can be obtained from an IoT monitoring system, enabling real-time collection and synchronous updating of static performance parameters and dynamic operating data.

[0033] As an example, data related to algal blooms can also include hydrological data, water quality data, spatial geographic data (high-precision GIS maps and underwater topographic data), and algal species information. Hydrological data includes water flow velocity, flow direction, water depth, and water level changes. Water quality data includes chlorophyll a concentration, algal density, total phosphorus, total nitrogen, ammonia nitrogen, pH, dissolved oxygen, and transparency, with chlorophyll a concentration being a key water quality parameter. Spatial geographic data includes high-precision maps of the water area, underwater topography, navigation channels, water intakes, and the locations of sensitive points such as scenic areas. Algal species information includes dominant algal species types and their characteristics.

[0034] In this embodiment, algae aggregation distribution maps can be periodically determined based on preprocessed multi-source data, such as hydrological data, water quality data, meteorological data, and spatial geographic data, thereby periodically updating the algae concentration within a preset future time period. The algae aggregation distribution map includes the distribution and concentration variation patterns of algae in the monitored water area within the preset future time period. The algae aggregation distribution map represents the predicted algae concentration at different spatial locations within the monitored water area during a preset future time period (e.g., 24 hours), and simultaneously identifies the location, occurrence time, and algae concentration of high-density areas, serving as the core basis for deploying algae removal devices. In this embodiment, conventional methods in the art, such as convection-diffusion-reaction equations and hydrodynamic-water quality coupling simulation methods, can be used to simulate the temporal and spatial distribution and changes of algae to obtain the algae aggregation distribution map.

[0035] As an example, the specific implementation method for determining the algal aggregation distribution map based on preprocessed multi-source data is as follows: Based on the high-precision GIS map and underwater topographic data of the monitored water area, the monitored water area is divided into finite element grid cells. Each grid cell is an independent analysis unit, covering the entire monitored water area without spatial overlap. In this embodiment, preprocessing can adopt conventional methods in the art, such as missing value imputation, normalization, standardization, outlier removal, etc., which will not be described in detail here.

[0036] Using a pre-calibrated algal migration and aggregation prediction model, pre-processed hydrological, water quality, meteorological, and spatial geographic data were used as model inputs. The convection-diffusion-reaction equation was used as the core equation to simulate the growth, migration, and diffusion patterns of algae. The core formula is: .

[0037] in, Indicates position and time Algae concentration at a given location, in units of (e.g., chlorophyll a concentration). This indicates the rate of change of algal concentration over time. This represents the water flow velocity vector, provided by hydrological data. This represents the turbulent diffusion coefficient. This indicates the source and sink, representing the combined effects of algal growth, death, and sedimentation. This represents the water volume within the finite element mesh. The turbulence diffusion coefficient and source / sink terms are components that need to be calibrated in the model.

[0038] Source and sink terms of core parameters of the model Rate setting, source and sink items ,in For algal growth rate, This represents the maximum growth rate of algae. The temperature effect function (input water temperature data) This is the illumination effect function (input illumination intensity data). The influence functions for nitrogen and phosphorus (input total nitrogen and total phosphorus data) are as follows: Algal respiration rate, This represents the algal settling rate. The maximum algal growth rate was obtained by culturing and monitoring dominant algal species in the water in a laboratory setting; a typical range is [range missing]. Algal respiration rate, typically ranging from [value range missing]. This can be adjusted with temperature. Algal settling rate: Single-celled cyanobacteria: Colonial Microcystis: Diatoms: The temperature effect function can be described using the modified Steele model, with values ​​ranging from [0, 1]. The optimal growth temperature for cyanobacteria is typically 25-30℃. The light effect function uses the Steele light suppression model to describe the effect of light intensity on photosynthesis, with values ​​ranging from [0, 1]. The optimal light intensity is typically... The nitrogen and phosphorus influence functions are described by the Monod equation, which describes the limiting effect of total nitrogen and total phosphorus concentrations on growth, with values ​​ranging from [0, 1].

[0039] As an example, a period with actual algal concentration monitoring data is selected as the calibration period. Historical algal bloom data and corresponding actual algal concentrations are collected. The initial preset parameters of the model (initial values ​​of turbulent diffusion coefficient, maximum algal growth rate, algal respiration rate, and algal settling rate) are substituted into the calculation to obtain the simulated algal concentration (predicted algal concentration value). The simulated algal concentration is compared with the actual algal concentration. The above parameters are adjusted iteratively to ensure that the evaluation indicators meet the preset conditions, such as the Nash efficiency coefficient being greater than or equal to the preset Nash efficiency coefficient and the root mean square error being greater than or equal to the preset root mean square error. The model parameter calibration is then completed, and the calibrated model is obtained.

[0040] This embodiment inputs preprocessed multi-source data into a calibrated model to obtain predicted algae concentration values ​​for each finite element grid cell, and generates an algae aggregation distribution map based on the predicted values. This achieves accurate prediction of algal bloom trends, clarifies the spatial and temporal characteristics of high-density areas, and solves the problems of lack of accurate prediction and poor targeted treatment in traditional algal bloom management, providing a quantitative basis for the precise deployment of algae removal devices.

[0041] The algae aggregation distribution map includes the algae concentration of each finite element mesh element, and high-density and low-density areas divided according to the algae concentration. For example, finite element mesh elements with an algae concentration greater than or equal to a preset density value are designated as high-density areas, which are the key deployment areas for algae removal devices. High-density areas are regions where the algae concentration is greater than or equal to the preset density value within a preset time period in the future.

[0042] S202: Determine the water temperature prediction curve based on meteorological data.

[0043] In this embodiment of the application, as an example, the specific implementation method for determining the water temperature prediction curve based on meteorological data is as follows: The predicted water temperature for a preset future time period is extracted from the meteorological data. Based on the predicted water temperature, a water temperature prediction curve is plotted with time (hours / minutes) as the abscissa and the predicted water temperature value (°C) as the ordinate. A confidence interval, such as 80%, can also be calculated for the water temperature prediction curve. The confidence interval can be calculated based on the error between historical actual water temperatures and the corresponding predicted water temperatures.

[0044] When a single high-density or low-density zone comprises multiple grid cells, the water temperature prediction curves for each zone can be determined by calculating the average water temperature of that zone. Alternatively, the average algae density can be calculated as the algae density for each zone.

[0045] S203: Based on satellite remote sensing data, water temperature prediction curves, and device parameters, determine the deployment plan for algae removal devices with the goal of minimizing the total treatment cost.

[0046] In this application embodiment, the deployment scheme of the algae removal device includes: the device type, the number of devices deployed, and the deployment location (latitude and longitude coordinates).

[0047] S204: Based on the deployment scheme of the algae removal device, the operating parameters of the algae removal device are determined with the goal of minimizing total energy consumption.

[0048] In this embodiment of the application, the operating parameters of the algae removal device include: instantaneous processing flow rate.

[0049] S205: Control the operation of the algae removal device according to its operating parameters.

[0050] In this embodiment of the application, as an example, the specific implementation method of controlling the operation of the algae removal device according to the operating parameters of the algae removal device is as follows: the operating parameters of the algae removal device are standardized and parsed to determine the corresponding control instructions. The control instructions adopt the Modbus general communication protocol and are adapted to the programmable logic controller of the algae removal device.

[0051] As an example, based on the deployment locations in the algae removal device deployment plan, navigation commands are sent to each algae removal device via a GPS positioning system, guiding the corresponding algae removal device to automatically navigate to the deployment location and complete the deployment of the device. The control system of the algae removal device generates corresponding control commands based on the operating parameters and sends them to the algae removal device. The algae removal device receives and executes the control commands, automatically loads the operating parameters, and sets the corresponding working mode. For example, the ultrasonic algae removal device switches to the constant temperature suppression mode and starts operating according to the set operating power and frequency. The algae removal device carries out algae bloom removal operations according to the set daily workload and operating frequency. The pump-suction algae removal device operates according to the set pump speed and instantaneous processing flow rate.

[0052] A collaborative control mechanism for algae removal devices can also be established, using an IoT monitoring system to collect and synchronize the operating status of each device in real time, ensuring that multiple devices operate collaboratively according to preset operating parameters and avoiding overlapping work areas. Simultaneously, a parameter deviation warning mechanism is set up; if the actual operating parameters of the device deviate from the set parameters by more than a threshold, an early warning is immediately issued and automatic correction is performed.

[0053] S206: During the operation of the algae removal device, the real-time water temperature of the monitored water area is obtained.

[0054] In this embodiment of the application, as an example, the specific implementation method for obtaining the real-time water temperature of the monitored water area is as follows: the real-time water temperature of the monitored water area can be obtained by setting a water temperature sensor.

[0055] S207: Adjust the operating parameters of the algae removal device according to the real-time water temperature.

[0056] In one possible implementation, S203 determines the deployment scheme of the algae removal device based on satellite remote sensing data, water temperature prediction curves, and device parameters, with the goal of minimizing the total treatment cost, including: Sa1 to Sa2.

[0057] Sa1: The first model is pre-built.

[0058] Sa2: Solve the first model based on satellite remote sensing data, water temperature prediction curves and device parameters to obtain the deployment scheme of the algae removal device.

[0059] In this embodiment, the first model includes a first objective function and first constraints. The first objective function is constructed with the goal of minimizing the total governance cost, which includes the operating cost of the algae removal device and the penalty cost for delays in governance progress.

[0060] In this embodiment of the application, the first constraints include: the quantity constraint of algae removal devices, the setting constraint of algae removal devices, and the cost upper limit constraint.

[0061] As an example, the constraint on the number of algae removal devices is: the number of algae removal devices... satisfy ,in The total area of ​​algal bloom, This refers to the daily operating area (m² / day) of a single unit in the device parameters. The algae removal device's operational capacity covers the total area of ​​algal blooms. The maximum number of algae removal devices is determined based on the quantity constraints.

[0062] In this embodiment, the constraints for setting up the algae removal devices include: the density of algae removal devices in high-density areas is greater than or equal to a first preset value. The algae density in the high-density areas is greater than or equal to the preset density value. The algae density is obtained based on an algae aggregation distribution map. The algae aggregation distribution map includes the distribution and concentration variation patterns of algae in the monitored water area over a preset future time period. Areas with algae densities greater than or equal to the preset density value are defined as high-density areas, and areas with algae densities less than the preset density value are defined as low-density areas, thereby dividing the algal bloom area into different density zones.

[0063] The constraints for setting up algae removal devices also include: when the water temperature is higher than a preset water temperature threshold (e.g., 25℃) based on the water temperature prediction curve, the type of algae removal device is an ultrasonic algae removal device, i.e., an algae removal device with high cooling efficiency. When the water temperature is lower than or equal to the preset water temperature threshold, the type of algae removal device is a retrieval algae removal device, i.e., an algae removal device with low cooling efficiency. Based on the algae aggregation distribution map, algae removal devices should be preferentially set up in areas with high algae concentrations to ensure that treatment resources are tilted towards high-density areas. The location of the algae removal devices must be within a deployable area, such as non-navigable channels and non-restricted navigation zones. The placement of the algae removal devices can be preset based on spatial geographic data, allowing them to be evenly distributed within the density area.

[0064] As an example, the cost ceiling constraint is: the total cost is less than or equal to the preset ceiling.

[0065] For a single density zone, under the condition that the installation density of algae removal devices in the high-density zone is greater than or equal to a first preset value, the type of algae removal device corresponding to the density zone is determined according to the installation constraints of the algae removal devices. The maximum number of algae removal devices corresponding to the density zone is determined based on the daily operating area of ​​a single algae removal device and the total area of ​​algal blooms corresponding to the density zone.

[0066] The total cost can be calculated based on the maximum number of algae removal devices corresponding to each density zone. It can then be determined whether the cost ceiling constraint is met. If it is, the algae removal devices are evenly distributed within the density zones to determine their placement, thus obtaining candidate deployment schemes for each density zone. If the constraint is not met, a combination scheme for the number of algae removal devices is used.

[0067] The number of algae removal devices for each density zone is adjusted, the total cost is recalculated, and it is determined whether the cost ceiling constraint is met. If it is met, the algae removal devices are evenly distributed within the density zones, and their placement is determined, thus obtaining candidate deployment schemes for each density zone. If the constraint is not met, the number combination schemes are eliminated. This results in multiple candidate deployment schemes, from which the scheme with the lowest total cost is selected as the final deployment scheme.

[0068] In this application embodiment, there are several ways to calculate the total cost: As an example, minimize the total cost: The first objective function (the formula for calculating total cost) is: .

[0069] As another example, minimizing the total cost: The first objective function (the formula for calculating total cost) is: .in, The construction period is in days. The number of types of algae removal devices, During the construction period Time of the first The operating cost function of algae removal devices Indicating the construction period Time of the first Operating power of algae removal devices , , For the first The constant term corresponding to the algae removal device, The penalty coefficient for delays in governance progress is an empirical value (e.g., a 10% increase in cost for each day of delay). After treatment The target algal bloom area at any given time is a preset value, which is the target value for algal bloom area control that is expected to be achieved during the treatment process. for The real-time algal bloom area at any given moment (calculated from satellite remote sensing data). This indicates that the governance results have met or exceeded the targets. .

[0070] As another example, the first model includes a first objective function and first constraints. The first objective function is constructed with the objectives of minimizing the total cost of treatment and maximizing the algae enrichment in the coverage area of ​​the algae removal device. . , The weights for total cost and algal enrichment are respectively, for example, both are 0.5. , The set of grid cells representing the function of the algae removal device. This represents the algae enrichment in the i-th grid cell.

[0071] As an example, determining the real-time algal bloom area based on satellite remote sensing data includes: Determine the atmospheric reflectance data corresponding to each pixel in the satellite remote sensing data.

[0072] The dynamic difference value of each pixel is determined based on the atmospheric reflectance data corresponding to each pixel.

[0073] Algal blooms are identified based on dynamic difference values ​​to determine the real-time algal bloom area. Pixels with dynamic difference values ​​greater than or equal to a preset difference value are identified as having algal blooms. The number of pixels with algal blooms is determined, and the real-time algal bloom area is calculated as the product of the water area corresponding to each pixel and the number of pixels with algal blooms.

[0074] In this embodiment of the application, the formula for calculating the dynamic difference value is: .

[0075] Where F represents the dynamic difference value, BG is the reflectance in the blue-green light band, RED is the reflectance in the red light band, k represents the water temperature correction coefficient, which is a preset value that characterizes the degree of influence of water temperature on algal growth. The value range is usually 0.02-0.1, obtained through calibration using historical algal bloom samples, and T represents the real-time water temperature. This indicates the reference water temperature, which is a preset value, such as the reference temperature for algae growth.

[0076] In one possible implementation, S204 determines the operating parameters of the algae removal device based on the deployment scheme of the algae removal device with the goal of minimizing total energy consumption, including: Sb1 to Sb2.

[0077] Sb1: The second model is pre-built.

[0078] In this embodiment, the second model includes a second objective function and a second constraint. The second objective function is constructed with the goal of minimizing total energy consumption.

[0079] In this embodiment of the application, the second constraint includes: processing capacity constraint and governance effect constraint.

[0080] In this embodiment of the application, the minimum total energy consumption is: , The second objective function is:

[0081] in, Indicates total energy consumption. The number is for the algae removal device, with a value from 1 to... , This refers to the total number of algae removal devices determined according to the deployment plan; The pre-set cycle for algal bloom control; The preset energy consumption per unit flow rate of the algae removal device; different types of algae removal devices. The value is a fixed preset parameter; For the first Taiwan algae removal device in time The instantaneous processing flow rate is the core solution variable of the model.

[0082] In this embodiment of the application, the processing capacity constraint is as follows: ,in This constraint, which sets the maximum processing capacity for different types of algae removal devices, prevents the devices from operating under overload, ensures the stability and lifespan of the equipment, and guarantees that the processing capacity of the device matches its own performance.

[0083] In this embodiment of the application, the constraints on the governance effect are: ,in For the first The algae concentration prediction curve at the specific location of the algae removal device reflects the algae concentration change pattern at different times during the treatment cycle. For the first The device is set in a cycle. The total amount of algae to be removed is preset based on the overall algal bloom control target, ensuring that the operation of a single device can achieve the predetermined algae removal effect, thereby guaranteeing the achievement of the overall algal bloom control target.

[0084] Sb2: Solve the second model based on the deployment scheme of the algae removal device to obtain the operating parameters of the algae removal device.

[0085] In this embodiment, under the premise of satisfying the second constraint, the instantaneous processing flow rate of each algae removal device at different times during the treatment cycle is obtained. If the pump speed is the core operating parameter of the algae removal device, the appropriate pump speed parameter can be obtained based on the correspondence between the instantaneous processing flow rate and the pump speed. The aforementioned instantaneous processing flow rate, pump speed, etc., are the operating parameters of the algae removal device. This achieves precise matching between the operating parameters and the device layout location and the dynamic changes in algae concentration, minimizing energy consumption while meeting the treatment effect, and improving the economy and precision of algal bloom control.

[0086] In one possible implementation, S207 adjusts the operating parameters of the algae removal device according to the real-time water temperature, including: Sc1 to Sc2.

[0087] Sc1: Calculates the water temperature deviation based on the real-time water temperature and the preset target water temperature.

[0088] Sc2: Adjust operating parameters based on water temperature deviation.

[0089] In this embodiment, it can be determined whether the water temperature deviation is greater than a water temperature deviation threshold. If it is greater than the threshold, the operating parameters are adjusted. If it is less than or equal to the threshold, no adjustment is needed. For example, the preset target water temperature is 20°C, and the water temperature deviation threshold is 2°C. The adjusted instantaneous processing flow rate always meets the constraints of processing capacity and treatment effect.

[0090] In this embodiment, adjusting the operating parameters according to the water temperature deviation can stabilize the water temperature in the optimal range for inhibiting algal growth, thereby effectively suppressing algal blooms.

[0091] In this embodiment of the application, adjusting the operating parameters (instantaneous processing flow rate) based on the water temperature deviation specifically includes: When the water temperature deviates At that time, if If the real-time water temperature is greater than the preset target water temperature and the deviation is greater than the water temperature deviation threshold, the instantaneous processing flow rate is increased in steps according to the deviation range. For every 1°C exceeding the water temperature deviation threshold, the current instantaneous processing flow rate is increased by 20% based on the baseline value (preset value) (provided the real-time water temperature is greater than the preset target water temperature), and the increased flow rate does not exceed the maximum processing capacity of the device. This is achieved by increasing the water treatment flow rate to enhance water agitation and break up water temperature stratification, thus realizing rapid cooling. If... If the real-time water temperature is lower than the preset target water temperature and the deviation exceeds the water temperature deviation threshold, the instantaneous processing flow rate is reduced in steps according to the deviation range. For every 1°C exceeding the water temperature deviation threshold, the current instantaneous processing flow rate is reduced by 20% from the baseline value (if the real-time water temperature is lower than the preset target water temperature). After the reduction, the flow rate will not be lower than the minimum stable operating flow rate of the device, thereby reducing water disturbance, preventing further water temperature decrease, and ensuring a balance between aquatic ecology and algal bloom suppression. At the same time, the instantaneous processing flow rate of the algae removal device should be maintained unchanged to ensure stable operation, balancing treatment effectiveness with efficient use of operating energy. All adjustment operations can be performed on a rolling basis in 5-minute cycles.

[0092] In one possible implementation, after controlling the operation of the algae removal device according to its operating parameters, the method further includes: The monitoring indicators for the monitored water area are obtained. These indicators include algal bloom area, real-time water temperature, and operational status data of the algae removal device. Specifically, the algal bloom area is calculated in real time using satellite remote sensing data. Following the algal bloom area calculation method described in this application, infrared remote sensing images are acquired every 2 hours. After preprocessing and feature extraction, the real-time algal bloom area is obtained, reflecting the core effect of algal bloom control. Real-time water temperature is collected by IoT water temperature sensors deployed at different locations in the monitored water area. The collection frequency is consistent with the monitoring cycle of the third model, which is once every 5 minutes, reflecting the dynamic changes in water temperature and the cooling and suppression effect. The operational status data of the algae removal device includes the actual operating power of the device, instantaneous processing flow rate, operating frequency, equipment start-up and shutdown status, and fault alarm information. This data is collected in real time through the device's IoT monitoring system, reflecting the actual operating status and working efficiency of the device.

[0093] The pre-set acceptance criteria are determined based on monitoring indicators. Specifically, the pre-set acceptance criteria are a multi-dimensional comprehensive evaluation standard, with core components including algal bloom control effectiveness standards, water temperature control standards, and equipment operation standards: The algal bloom control effectiveness standard is that the algal bloom area reduction rate within the control period is ≥ a pre-set percentage (e.g., 90%), and the real-time algal bloom area remains stable below the pre-set threshold. The water temperature control standard is that the average water temperature remains stable at the pre-set target water temperature. Within the specified range, and the duration of water temperature deviation from the safe range is ≤5%. The operational standards for the device are: utilization rate of the algae removal device ≥ a preset ratio (e.g., 80%), equipment failure rate ≤ a preset threshold (e.g., 5%), and no equipment overload. If all the above monitoring indicators meet the preset acceptance standards, the algae bloom control is deemed to have achieved the expected results, and the device operation intensity can be gradually reduced or normalized monitoring can begin. If any monitoring indicator fails to meet the preset acceptance standards, a re-optimization mechanism is triggered, recalculating the algae bloom identification difference value, adjusting the device deployment plan or operating parameters, until the monitoring indicators meet the acceptance standards. This step constructs a closed-loop verification system for algae bloom control, enabling timely detection and optimization of problems during the control process, ensuring that the algae bloom control effect achieves the predetermined goals.

[0094] As an example, the method for controlling algal blooms in a certain lake is as follows: S1: Multi-source data acquisition: Satellite remote sensing data (remote sensing images), meteorological data, etc., are stored in a cloud database, and system parameters are initialized: algal bloom identification threshold. Daily operating area of ​​a single algae removal device (Based on the performance of the ultrasonic algae removal vessel: it can process 500m² per hour and works 8 hours a day).

[0095] S2: Remote sensing image preprocessing: The original remote sensing images are corrected and optimized to eliminate interference from external factors and improve the accuracy of algal bloom identification. This step, through atmospheric correction and geometric fine correction, effectively eliminates the interference of factors such as sensors, atmosphere, and terrain on remote sensing data, ensuring the accuracy and reliability of multispectral reflectance data, and establishing a unified spatiotemporal data benchmark, providing a high-quality data foundation for subsequent accurate algal bloom identification.

[0096] S21: Atmospheric Correction Using a dark object correction model to process remote sensing images: 1. Select dark object regions in the image with near-zero reflectivity (such as deep water areas) and calculate the minimum radiance values ​​for each band. .

[0097] 2. Calculate the true surface reflectance using the formula. : .

[0098] in, For sensor radiance, This is the Earth-Sun distance correction factor. Solar irradiance, This is the solar zenith angle.

[0099] 3. Output corrected reflectance data (BG, RED, G, NIR bands). For example, corrected NIR band reflectance data is used to characterize algal biomass (high values ​​indicate algal blooms).

[0100] S22: Geometric fine correction: A third-generation geometric calibration model is used to correct deviations caused by sensor attitude and Earth's rotation. The projection is unified to the WGS-84 coordinate system.

[0101] The calibration accuracy is controlled within 1 pixel (i.e. ±2 meters).

[0102] S23: Image cropping and noise reduction: Cropping a remote sensing image to retain only the effective area of ​​a lake (removing redundant information from surrounding land). Applying Gaussian filtering for image denoising to reduce cloud or wave interference. Example: The cropped image is reduced to 800×600 pixels, focusing on the high-incidence area of ​​algal blooms in the western part of a lake.

[0103] S3: Algal bloom feature extraction and area calculation: Based on preprocessed data, algal bloom areas are identified and their areas are quantified, providing a basis for device deployment. This step achieves pixel-level accurate algal bloom identification by constructing a dynamic difference value model that integrates algal index and water temperature factor, effectively distinguishing algal bloom areas from normal water bodies. At the same time, by combining spatial resolution and pixel statistical methods, the coverage area of ​​algal blooms is accurately quantified, enabling the system to accurately assess the scale and spatial distribution characteristics of algal blooms, providing a reliable quantitative basis for subsequent governance decisions.

[0104] S31: Reflectance Data Extraction: Obtain band reflectance data for each pixel: BG, RED, G, NIR. Reflectance data reflects the optical characteristics of water bodies (e.g., high NIR reflectance indicates algal enrichment).

[0105] S32: Calculate the difference value .

[0106] S33: Generate a binary map of algal bloom: Difference value With threshold Compare: like The pixel is marked as 1 (water bloom pixel).

[0107] like The pixel is marked as 0 (non-aqua bloom pixel).

[0108] Output a binary image: white areas (pixel value 1) represent algal bloom areas, and black areas (pixel value 0) represent normal water areas. Example: 50,000 algal bloom pixels were identified in an image of a lake.

[0109] This application starts from the initial threshold Pixel-level calculations begin. If the identification results do not conform to the spatial distribution characteristics of algal blooms, an adaptive threshold adjustment mechanism is employed. This mechanism iteratively expands the threshold range with a preset step size until an algal bloom distribution map that meets spatial continuity requirements is obtained. If the adjustment exceeds the maximum allowable range and still no valid result is obtained, it is determined that no algal blooms occurred during that period.

[0110] S34: Calculate the area of ​​algal bloom Based on pixel count and spatial resolution: .

[0111] in, For the number of pixels of water bloom, For spatial resolution (2 meters). For example, ,but: (20 hectares)

[0112] S4: Algae Removal Device Quantity Calculation and Deployment Strategy Based on the area of ​​algal bloom and the treatment period, the equipment layout scheme is optimized to achieve efficient resource scheduling. This step intelligently calculates the optimal number of equipment by combining algal bloom area data, equipment operating efficiency and treatment period requirements, and generates the optimal layout scheme based on the spatial density distribution of algal bloom using the weighted Voronoi algorithm. This ensures that each layout point can cover the algal bloom area with the highest density, realizing efficient allocation of treatment resources and optimized spatial layout. This enables the system to achieve maximum treatment effect with limited resources, improving the efficiency and economy of algal bloom treatment.

[0113] S41: Calculation of the number of devices n: formula: .

[0114] in For the target construction period (days), This refers to the daily operating area of ​​a single unit (4,000 m² / day). For example, setting a construction period. Heaven, then: tower.

[0115] S42: Deployment Optimization: Combined with a dynamic allocation device for bloom density (pixel clustering in a binary image): Prioritize the deployment of devices in high-density areas (e.g., deploy 1 unit per hectare).

[0116] Device type: Ultrasonic algae removal boat (which achieves cooling and inhibition by agitating the water to disrupt water temperature stratification).

[0117] Example: 7 units are deployed in the high-density area (15 hectares) in the west of a lake, and 3 units are deployed in the low-density area (5 hectares) in the east.

[0118] S5: Device Deployment and Real-time Control: Execute deployment instructions and monitor the effects, dynamically adjust parameters to adapt to environmental changes. This step uses an unmanned vessel platform to achieve automatic deployment and collaborative operation of algae removal devices, realizing fully automated processing from identification to execution. At the same time, through periodic monitoring and dynamic evaluation mechanisms, the treatment effect can be tracked in real time and the deployment plan can be adjusted in a timely manner, enabling the system to adapt to the dynamic characteristics of algal blooms, ensuring the continuity and adaptability of the treatment effect, and greatly improving the intelligence level and execution efficiency of algal bloom treatment.

[0119] S51: Device Deployment and Start-up: Remotely deploy the ultrasonic algae removal vessel, setting the operating mode to "cooling suppression" (frequency 20–40kHz, reducing water temperature by 3–5°C). Navigate the device to the algal bloom area using the GPS positioning system.

[0120] S52: Real-time Monitoring and Feedback: Deploy IoT sensors to monitor water temperature and algal bloom area changes; acquire infrared remote sensing images every 2 hours and update algal bloom area. If the actual algal bloom area reduction rate is lower than expected (e.g., <10% / day), trigger re-optimization: recalculate the difference value. Alternatively, the threshold can be adjusted. Example: 24 hours after deployment, if the algal bloom area drops to 180,000 m², the system will automatically reduce the workload of one unit.

[0121] S6: Effect Verification: Evaluate the overall performance of the system to ensure that the goal of cooling and suppressing algal blooms is achieved. This step establishes a closed-loop verification system for algal bloom control, which can comprehensively evaluate the control effect, identify problems in a timely manner and optimize them, and ensure that algal bloom control achieves the expected goals.

[0122] Predicting the effectiveness of algal bloom control within a 5-day control period: The area of ​​algal blooms decreased by ≥95% (from 200,000 m² to 10,000 m²).

[0123] The average water temperature drops by 4°C (from 28°C to 24°C), inhibiting algae growth.

[0124] The equipment utilization rate reached 98%, and no resources were wasted.

[0125] Compared with the traditional method (uniformly distributed devices), this method improves efficiency by 30% and reduces treatment costs by 25%.

[0126] This specific embodiment integrates multispectral reflectance data and meteorological parameters to construct a dynamic difference value model for algal bloom identification. Through a hierarchical process (data acquisition → preprocessing → feature extraction → device optimization → real-time control → effect verification), it achieves accurate identification and efficient management of algal bloom areas. In a case study of a lake, this method, combining remote sensing technology with a multi-layered dynamic optimization model, solves the problem of blind application in large-area water management, improving the cooling and suppression effect by more than 20%, while simultaneously achieving multiple benefits such as a 25% reduction in management costs and a 30% increase in device utilization. The method of this application can be extended to the management of algal blooms in other eutrophic waters such as reservoirs and ponds, possessing broad application prospects and practical promotion value.

[0127] This application also provides an algal bloom control device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0128] This application provides an algal bloom control device. Figure 3 This is a structural block diagram of the algal bloom control device according to an embodiment of this application, such as... Figure 3 As shown, it includes: The first processing module is used to acquire data related to algal blooms in the monitored water area. This data includes satellite remote sensing data, meteorological data, and device parameters of the algae removal equipment.

[0129] The second processing module is used to determine the water temperature prediction curve based on meteorological data.

[0130] The third processing module is used to determine the deployment plan of the algae removal device based on satellite remote sensing data, water temperature prediction curves, and device parameters, with the goal of minimizing the total treatment cost.

[0131] The fourth processing module is used to determine the operating parameters of the algae removal device based on the deployment plan of the algae removal device with the goal of minimizing total energy consumption.

[0132] The fifth processing module is used to control the operation of the algae removal device based on its operating parameters.

[0133] The sixth processing module is used to acquire the real-time water temperature of the monitored water area during the operation of the algae removal device.

[0134] The seventh processing module is used to adjust the operating parameters of the algae removal device according to the real-time water temperature.

[0135] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0136] The following is a detailed reference. Figure 4 The diagram illustrates a structural schematic suitable for implementing the electronic device described in the embodiments of this application. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from memory 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0137] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although... Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0138] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program including program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a memory 508, or installed from a ROM 502. When the computer program is executed by the processor 501, it performs the functions defined in the algal bloom control method of embodiments of this application.

[0139] Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0140] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc. Further, the storage medium may also include combinations of the above types of memory. It is understood that computers, processors, microprocessors, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the algal bloom control method shown in the above embodiments is implemented.

[0141] A portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0142] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for controlling algal blooms, characterized in that, The method includes: Acquire data related to algal blooms in the monitored water area; the algal bloom-related data includes: satellite remote sensing data, meteorological data, and device parameters of the algae removal device. A water temperature prediction curve is determined based on the meteorological data; Based on the satellite remote sensing data, the water temperature prediction curve, and the device parameters, a deployment scheme for the algae removal device is determined with the goal of minimizing the total treatment cost. Based on the deployment scheme of the algae removal device, the operating parameters of the algae removal device are determined with the goal of minimizing total energy consumption; The operation of the algae removal device is controlled according to its operating parameters. During the operation of the algae removal device, the real-time water temperature of the monitored water area is acquired; The operating parameters of the algae removal device are adjusted according to the real-time water temperature.

2. The method according to claim 1, characterized in that, The process of determining a deployment scheme for the algae removal device based on the satellite remote sensing data, the water temperature prediction curve, and the device parameters, with the goal of minimizing the total treatment cost, includes: A first model is pre-constructed; the first model includes a first objective function and a first constraint; the first objective function is constructed with the goal of minimizing the total governance cost, which includes: the operating cost of the algae removal device and the penalty cost for delays in governance progress; The first constraint includes: the quantity constraint of algae removal devices, the setting constraint of algae removal devices, and the cost ceiling constraint; Based on the satellite remote sensing data, the water temperature prediction curve, and the device parameters, the first model is solved to obtain the deployment scheme of the algae removal device.

3. The method according to claim 1, characterized in that, The constraints for setting up the algae removal device include: the installation density of the algae removal device in the high-density area is greater than or equal to a first preset value; the algae density in the high-density area is greater than or equal to a preset density value; the algae density is obtained based on an algae aggregation distribution map; the algae aggregation distribution map includes the distribution and concentration variation patterns of algae in the monitored water area within a preset future time period.

4. The method according to claim 1, characterized in that, The deployment scheme based on the algae removal device determines the operating parameters of the algae removal device with the goal of minimizing total energy consumption, including: A second model is pre-constructed; the second model includes a second objective function and second constraints; the second objective function is constructed with the goal of minimizing total energy consumption. The second set of constraints includes: processing capacity constraints and governance effectiveness constraints; The second model is solved based on the deployment scheme of the algae removal device to obtain the operating parameters of the algae removal device.

5. The method according to claim 1, characterized in that, The adjustment of the operating parameters of the algae removal device based on the real-time water temperature includes: Calculate the water temperature deviation based on the real-time water temperature and the preset target water temperature; The operating parameters are adjusted based on the water temperature deviation.

6. The method according to claim 1, characterized in that, After controlling the operation of the algae removal device according to its operating parameters, the method further includes: Acquire monitoring indicators for the monitored water area; the monitoring indicators include algal bloom area, real-time water temperature, and operational status data of the algae removal device; Determine whether the preset acceptance criteria are met based on the monitoring indicators.

7. A device for controlling algal blooms, characterized in that, The device includes: The first processing module is used to acquire algal bloom-related data of the monitored water area; the algal bloom-related data includes: satellite remote sensing data, meteorological data, and device parameters of the algae removal device. The second processing module is used to determine the water temperature prediction curve based on the meteorological data; The third processing module is used to determine the deployment scheme of the algae removal device based on the satellite remote sensing data, the water temperature prediction curve and the device parameters, with the goal of minimizing the total treatment cost. The fourth processing module is used to determine the operating parameters of the algae removal device based on the deployment plan of the algae removal device with the goal of minimizing total energy consumption; The fifth processing module is used to control the operation of the algae removal device according to its operating parameters; The sixth processing module is used to acquire the real-time water temperature of the monitored water area during the operation of the algae removal device; The seventh processing module is used to adjust the operating parameters of the algae removal device according to the real-time water temperature.

8. An electronic device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the method of any one of claims 1 to 6.

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