A method, device, system and readable storage medium for controlling algae bloom in lakes and ponds

Abstract

The present invention discloses a method, device, system and readable storage medium for controlling algae blooms in lakes and ponds, wherein the method comprises the following steps: obtaining a pre-constructed water environment index association model of a lake and pond; the water environment index association model is constructed based on hydrological monitoring data, algae density and biomass monitoring data, water quality monitoring data, and floating leaf vegetation status monitoring data of a local in-situ test area where floating leaf vegetation is arranged in the lake and pond; based on the water environment index association model, determining the amount of algae to be removed in the lake and pond according to the water quality improvement target, and determining the amount of floating leaf vegetation status to be constructed in the lake and pond according to the amount of algae to be removed. By implementing the method of the present invention, long-term, efficient and high-precision algae control can be achieved for the target lake and pond, and the water quality improvement target can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method, device, system and readable storage medium for controlling algae blooms in lakes and ponds. Background Art

[0002] Over the years, the eutrophication and algal bloom of plain river networks and lakes have received widespread attention. 30% to 40% of lakes and reservoirs worldwide are affected by eutrophication to varying degrees. Eutrophication of water bodies leads to the massive reproduction of algae and the formation of algal blooms. A large number of studies have shown that the main factors that may affect the growth of algae are abiotic factors such as nutrients, temperature, light and hydrodynamic conditions, and biotic factors such as zooplankton predation. It is urgent to study and understand the causes of eutrophication of water bodies, formulate clearer water pollution prevention and control goals in the basin, and then alleviate eutrophication of water bodies or prevent and control algal blooms. Research work has been carried out at home and abroad on related issues, and a variety of algae removal technologies have been adopted.

[0003] For example, the physical techniques in the prior art include ultrasonic algae removal, filtration algae removal, microfiltration algae removal, shading technology algae removal, ultraviolet light irradiation algae removal, clay algae removal, etc. They have simple operating principles and can directly remove algae in the water without causing secondary pollution; the chemical techniques in the prior art include chemical oxidation, coagulation algae removal, and photocatalytic oxidation algae removal. They have low cost, quick effect, and good effect; the biological techniques in the prior art include algae control by aquatic higher plants, algae control by aquatic animals, algae control by allelopathic action, and algae removal by microorganisms. They have good effects on water purification and restoration and good ecological safety.

[0004] However, none of these treatment methods in the prior art can achieve long-term, effective, and high-precision algae control in waters that require algae control. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a method for controlling algae blooms in lakes and ponds, which is based on a water environment indicator correlation model constructed based on data from a local in-situ experimental area of ​​a lake and pond and is capable of long-term, effective and high-precision algae control, and provides corresponding devices, systems and readable storage media.

[0006] To this end, according to a first aspect, the present invention provides a method for controlling algae blooms in lakes and ponds, comprising the following steps:

[0007] Obtaining a pre-constructed water environment index correlation model of a lake; the water environment index correlation model is constructed based on hydrological monitoring data, algae density and biomass monitoring data, water quality monitoring data, and floating leaf vegetation status monitoring data of a local in-situ test area where floating leaf vegetation is deployed in the lake;

[0008] Based on the water environment index association model, the amount of algae to be removed in the lake is determined according to the water quality improvement target, and the state of floating leaf vegetation to be constructed in the lake is determined according to the amount of algae to be removed.

[0009] In an optional embodiment, the method for controlling algae blooms in lakes and ponds further comprises the following steps:

[0010] After a preset time, the current floating leaf vegetation status monitoring data of the lake is obtained, and the maximum algae removal amount of the lake is obtained based on the water environment index association model and the current floating leaf vegetation status monitoring data;

[0011] Obtain the current algae density and biomass monitoring data of the lake, and determine the amount of algae water that can be adjusted into the lake based on the maximum algae removal amount, current algae density and biomass monitoring data.

[0012] In an optional embodiment, the method for controlling algae blooms in lakes and ponds further comprises the following steps:

[0013] Acquire the algae density and biomass monitoring data of the lake in real time, and control the amount of algae water transferred into the lake based on the real-time algae density and biomass monitoring data, so that the real-time algae density and biomass monitoring data are less than the maximum algae removal amount.

[0014] In an optional embodiment, the water environment indicator association model can also be constructed based on hydrological monitoring data, algae density and biomass monitoring data, water quality monitoring data, floating leaf vegetation status monitoring data, and meteorological condition data of a local in-situ test area where floating leaf vegetation is deployed in a lake.

[0015] In an optional implementation, the hydrological monitoring data includes water depth monitoring data and water flow velocity measurement data.

[0016] In an optional embodiment, the floating leaf vegetation status data includes aquatic vegetation type data, aquatic vegetation coverage data and aquatic vegetation biomass data.

[0017] According to a second aspect, an embodiment of the present invention provides a lake water bloom algae control device, comprising:

[0018] A model acquisition module is used to acquire a pre-built lake water environment index association model; the water environment index association model is constructed based on hydrological monitoring data, algae density and biomass monitoring data, water quality monitoring data, and floating leaf vegetation status monitoring data of a local in-situ test area where floating leaf vegetation is deployed in the lake;

[0019] The algae removal calculation module is used to determine the amount of algae to be removed in the lake based on the water environment indicator association model and the water quality improvement target, and to determine the state of floating leaf vegetation to be constructed in the lake based on the amount of algae to be removed.

[0020] According to the third aspect, an embodiment of the present invention provides a lake water algae bloom control system, comprising: at least one processor; and a memory communicatively connected to the at least one processor; computer instructions are stored in the memory, and the processor executes the method described in the first aspect or any one of the embodiments of the first aspect by executing the computer instructions.

[0021] According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method described in the first aspect or any one of the embodiments of the first aspect.

[0022] The technical solution provided by the present invention has the following advantages:

[0023] 1. The present invention provides a method for controlling algae blooms in lakes and ponds. The inventors have found that the current algae removal technology is either directly implemented after a basic investigation of the target lake or pond, or is implemented after simulating the target lake or pond in the laboratory and conducting an algae removal experiment. However, even if as many samples as possible are collected from the lake or pond and the simulation is as comprehensive and detailed as possible, there will still be a large deviation from the actual scene, which will lead to the final algae control and algae removal effect being less than ideal; based on this, the inventors creatively adopted a method of conducting local in-situ experiments in the lake or pond to construct a water environment index correlation model, and then conducted an algae control method for controlling algae in the entire lake or pond based on the water environment index correlation model, thereby improving the algae control accuracy; and the model in this method is constructed based on the hydrological monitoring data, algae density and biomass monitoring data, water quality monitoring data, and floating leaf vegetation status monitoring data of a local in-situ test area where floating leaf vegetation is deployed in the lake or pond, which can accurately reflect the correlation between the water environment indicators of the corresponding lake or pond, and then the required floating leaf vegetation can be constructed according to the water environment index correlation model to carry out long-term effective high-precision algae control and achieve the goal of improving water quality.

[0024] 2. The method for controlling algae blooms in lakes and ponds provided by the present invention can further improve the accuracy of the water environment index correlation model by using meteorological condition data as one of the dimensions of the water environment index correlation model, thereby further improving the effectiveness of the method in controlling algae. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1A method flow chart of a method for controlling algae blooms in lakes provided by an embodiment of the present invention;

[0027] Figure 2 A method flow chart of another method for controlling algae blooms in lakes provided by an embodiment of the present invention;

[0028] Figure 3 A principle block diagram of a lake water bloom control algae device provided by an embodiment of the present invention;

[0029] Figure 4 A schematic diagram of the hardware structure of a lake water bloom control algae system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0031] In the description of the present invention, it should be noted that the terms “first”, “second” and “third” are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0032] Example 1

[0033] Figure 1 FIG. 1 is a flow chart showing a method for controlling algae bloom in lakes according to an embodiment of the present invention. Specifically, Figure 1 As shown, the method may include the following steps:

[0034] S100: Obtain a pre-constructed lake water environment indicator association model.

[0035] Specifically, the water environment index association model is constructed based on the hydrological monitoring data, algae density and biomass monitoring data, water quality monitoring data, and floating leaf vegetation status monitoring data of a local in-situ test area where floating leaf vegetation is deployed in a lake.

[0036] Specifically, the local in-situ test area can be one or multiple areas located at different positions in the lake, and their specific locations and numbers can be set according to the needs of the specific application scenario to ensure their representativeness.

[0037] Specifically, the hydrological monitoring data includes water depth monitoring data and water flow velocity measurement data. In specific implementation, the water depth monitoring data can be obtained by using radar water level monitors, ultrasonic water level meters, etc., and the water flow velocity measurement data can be obtained by using radar wave velocity detectors, drone water flow velocity measurement systems, Doppler current meters, etc.

[0038] Specifically, algae may include blue algae, green algae, etc. In specific implementation, the algae density and biomass monitoring data may be obtained by using an online optical blue-green algae sensor analyzer, or may be monitored in real time by video and optical sensors.

[0039] Specifically, the water quality monitoring data may include water temperature, dissolved oxygen, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand. In specific implementation, the water quality monitoring data may be obtained by using a water quality buoy to automatically detect each station.

[0040] Specifically, the floating leaf vegetation status data includes aquatic vegetation type data, aquatic vegetation coverage data and aquatic vegetation biomass data. In specific implementation, the floating leaf vegetation status data can be obtained through real-time monitoring of video and optical sensors.

[0041] Specifically, the water environment index association model can be constructed using the JPDA algorithm, the GNN algorithm, the Apriori algorithm, etc., and no specific restrictions are made in this embodiment.

[0042] S200: Based on the water environment index association model, the amount of algae to be removed in the lake is determined according to the water quality improvement target, and the state of floating leaf vegetation to be constructed in the lake is determined according to the amount of algae to be removed.

[0043] Specifically, when the construction of floating leaf vegetation that meets the state quantity of floating leaf vegetation to be constructed is completed in the lake, effective algae removal and control can be achieved in the lake (generally, the algae removal effect can be achieved after one algae removal cycle).

[0044] As an optional specific implementation of this embodiment, after a period of time (generally an algae removal cycle, such as two to three weeks), the algae in the original algae water in the lake will have been effectively suppressed. At this time, in order to give full play to the algae control effect of the floating leaf vegetation that has been constructed in the lake, Figure 2 As shown, based on the above steps S100 to S200, the method in this embodiment can be set to further include the following steps:

[0045] S300: After a preset time, the current floating leaf vegetation status monitoring data of the lake is obtained, and the maximum algae removal amount of the lake is obtained based on the water environment index association model and the current floating leaf vegetation status monitoring data.

[0046] Specifically, the preset time is set according to the algae removal cycle of the floating leaf vegetation that has been constructed in the lake; illustratively, if the algae removal cycle is two weeks, the preset time can be set to two weeks or more.

[0047] S400: Obtain the current algae density and biomass monitoring data of the lake, and determine the amount of algae water that can be adjusted into the lake based on the maximum algae removal amount, the current algae density and biomass monitoring data.

[0048] As an optional specific implementation of this embodiment, in order to ensure the algae control effect of the lake water bloom control method in this embodiment, the amount of water transferred into the lake can be controlled in real time to ensure that the algae in the lake does not exceed the maximum algae removal amount of the floating leaf vegetation that has been constructed, that is, Figure 2 As shown, based on the above steps S100 to S400, the method of this embodiment may further include the following steps:

[0049] S500: Acquire the algae density and biomass monitoring data of the lake in real time, and control the amount of algae water transferred into the lake according to the real-time algae density and biomass monitoring data, so that the real-time algae density and biomass monitoring data are less than the maximum algae removal amount.

[0050] Specifically, when the real-time algae density and biomass monitoring data are greater than the maximum algae removal amount, and the difference reaches a first preset value, the amount of water transferred into the lake from the algae-controlled water area is reduced.

[0051] In specific implementation, the amount of water transferred in can also be increased when it is too small. That is, when the real-time algae density and biomass monitoring data are less than the maximum algae removal amount, and the difference reaches a second preset value, the amount of water transferred into the lake from the algae-controlled water area can be increased.

[0052] In specific implementation, the first preset value and the second preset value can be set according to the correlation between the floating leaf vegetation status monitoring data and the algae density and biomass monitoring number, and can be specifically adjusted according to the needs of the actual application scenario.

[0053] In an optional specific embodiment, based on the inventor's discovery that meteorological condition data also has a certain degree of influence on the algae control effect, therefore, in order to further improve the algae control effectiveness of the method in this embodiment, the meteorological condition data can be set as one-dimensional correlation data of the water environment indicator correlation model, that is, the water environment indicator correlation model in this embodiment can also be constructed based on the hydrological monitoring data of the local in-situ test area where floating leaf vegetation is laid out in the lake, algae density and biomass monitoring data, water quality monitoring data, floating leaf vegetation status monitoring data, and meteorological condition data.

[0054] Specifically, the meteorological condition data may include temperature data, rainfall data, radiation data, etc. In specific implementation, the meteorological condition data may be obtained by monitoring a small meteorological station built at the lake.

[0055] In summary, the inventors have found that the current algae removal technology is either directly implemented after a basic investigation of the target lake or implemented after simulating the target lake in the laboratory and conducting algae removal experiments. However, even if as many samples as possible are collected from the lake and the simulation is as comprehensive and detailed as possible, there will still be a large deviation from the actual scene, which will lead to the final algae control and algae removal effect being less than ideal; therefore, the inventors creatively adopted a local in-situ experiment in the lake to construct a water environment indicator correlation model, and then carried out an algae control method for the entire lake based on the water environment indicator correlation model to improve the algae control accuracy; and the model in this embodiment is constructed based on the hydrological monitoring data, algae density and biomass monitoring data, water quality monitoring data, and floating leaf vegetation status monitoring data of the local in-situ test area where floating leaf vegetation is deployed in the lake, which can accurately reflect the correlation between the water environment indicators of the corresponding lake, and then the required floating leaf vegetation can be constructed according to the water environment indicator correlation model to carry out long-term, effective and high-precision algae control and achieve the goal of improving water quality.

[0056] Example 2

[0057] The embodiment of the present invention provides a lake water bloom control algae device, such as Figure 3 As shown, the device includes: a model acquisition module 10 and a calculation and algae removal module 20.

[0058] The model acquisition module 10 is used to obtain a pre-constructed lake water environment index association model; the water environment index association model is constructed based on the hydrological monitoring data, algae density and biomass monitoring data, water quality monitoring data, and floating leaf vegetation status monitoring data of a local in-situ test area where floating leaf vegetation is arranged in the lake;

[0059] The algae removal calculation module 20 is used to determine the amount of algae to be removed in the lake according to the water quality improvement target based on the water environment index association model, and determine the floating leaf vegetation state quantity to be constructed in the lake according to the amount of algae to be removed.

[0060] The further functional description of each of the above modules is the same as that in the above embodiment 1 and will not be repeated here.

[0061] The embodiment of the present invention also provides a lake water bloom control algae system, such as Figure 4 As shown, the system may include a processor 41 and a memory 42, wherein the processor 41 and the memory 42 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0062] The processor 41 may be a central processing unit (CPU). The processor 41 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0063] The memory 42 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the network security assessment method in Example 1 of the present invention. The processor 41 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 42, that is, the lake water bloom control algae method in the above method embodiment 1 is implemented.

[0064] The memory 42 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function; the data storage area may store data created by the processor 41, etc. In addition, the memory 42 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 42 may optionally include a memory remotely arranged relative to the processor 41, and these remote memories may be connected to the processor 41 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0065] The one or more modules are stored in the memory 42 and when executed by the processor 41, perform the following steps: Figure 1-Figure 2 The method for controlling algae blooms in lakes and ponds in Example 1 is shown.

[0066] For details of the above lake water bloom control algae system, please refer to Figure 1 to Figure 2 The corresponding related descriptions and effects in the embodiment 1 shown are understood and will not be repeated here.

[0067] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0068] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for controlling algae blooms in lakes and ponds, characterized in that: The steps include: Obtaining a pre-constructed water environment index correlation model of the lake; the water environment index correlation model is constructed based on a local in-situ test in the lake; floating leaf vegetation is arranged in the local in-situ test area, and the water environment index correlation model is constructed based on hydrological monitoring data, algae density and biomass monitoring data, water quality monitoring data, and floating leaf vegetation status monitoring data of the local in-situ test area; Based on the water environment index association model, the amount of algae to be removed from the lake is determined according to the water quality improvement target, and the floating leaf vegetation state to be constructed in the lake is determined according to the amount of algae to be removed; After a preset time, current floating leaf vegetation status monitoring data of the lake is obtained, and the maximum algae removal amount of the lake is obtained based on the water environment index association model and the current floating leaf vegetation status monitoring data; The current algae density and biomass monitoring data of the lake are obtained, and the amount of algae water that can be adjusted into the lake is determined according to the maximum algae removal amount, the current algae density and biomass monitoring data.

2. The method for controlling algae blooms in lakes and ponds according to claim 1, characterized in that: The following steps are also included: The algae density and biomass monitoring data of the lake are acquired in real time, and the amount of algae water transferred into the lake is controlled according to the real-time algae density and biomass monitoring data, so that the real-time algae density and biomass monitoring data are less than the maximum algae removal amount.

3. The method for controlling algae blooms in lakes and ponds according to claim 1 or 2, characterized in that: The water environment index association model is constructed based on the hydrological monitoring data of the local in-situ test area where floating leaf vegetation is deployed in the lake, the algae density and biomass monitoring data, the water quality monitoring data, the floating leaf vegetation status monitoring data, and the meteorological condition data.

4. The method for controlling algae blooms in lakes and ponds according to claim 3, characterized in that: The hydrological monitoring data includes water depth monitoring data and water flow velocity measurement data.

5. The method for controlling algae bloom in lakes and ponds according to claim 3, characterized in that: The floating leaf vegetation status monitoring data includes aquatic vegetation type data, aquatic vegetation coverage data and aquatic vegetation biomass data.

6. A lake water bloom control algae device, characterized in that: include: A model acquisition module is used to acquire a pre-constructed water environment index association model of the lake; the water environment index association model is constructed based on a local in-situ test in the lake; floating leaf vegetation is arranged in the local in-situ test area, and the water environment index association model is constructed based on the hydrological monitoring data, algae density and biomass monitoring data, water quality monitoring data, and floating leaf vegetation status monitoring data of the local in-situ test area; an algae removal calculation module, for determining the amount of algae to be removed from the lake according to the water quality improvement target based on the water environment index association model, and determining the amount of floating leaf vegetation state to be constructed in the lake according to the amount of algae to be removed; A data monitoring module, for obtaining the current floating leaf vegetation status monitoring data of the lake after a preset time, and obtaining the maximum algae removal amount of the lake based on the water environment index association model and the current floating leaf vegetation status monitoring data; The algae water amount calculation module is used to obtain the current algae density and biomass monitoring data of the lake, and determine the amount of algae water that can be adjusted into the lake based on the maximum algae removal amount, the current algae density and biomass monitoring data.

7. A lake water bloom control algae system, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the one processor, and the instructions are executed by the at least one processor so that the at least one processor executes any of the methods described in claims 1-5 above.

8. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by a processor, the steps of the method described in any one of claims 1 to 5 are implemented.

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