A water pollution prevention and control method and system based on an ecological floating bed

Abstract

The present application provides a water pollution prevention and control method and system based on an ecological floating bed, which relates to the technical field of water pollution prevention and control. The key points of its technical solution are as follows: obtaining water quality parameters, plant growth conditions, and water level change information; calculating the water quality purification efficiency according to the water quality parameters, plant growth conditions, and water level change information; determining the floating bed flipping frequency, flipping speed, and floating bed height according to the difference between the water quality purification efficiency and the preset target efficiency; controlling the floating bed to perform the flipping action with the flipping frequency and flipping speed and the adjustment action of the floating bed height, so as to improve the water quality purification efficiency and minimize the interference to the aquatic ecosystem. The water pollution prevention and control method and system based on an ecological floating bed provided by the present application have the advantages of being able to adapt to dynamically changing water quality and seasonal pollutant concentration fluctuations and minimizing the impact on the aquatic ecosystem.

Description

Technical Field

[0001] The present application relates to the technical field of water pollution prevention and control, and in particular to a water pollution prevention and control method and system based on an ecological floating bed. Background Art

[0002] In a river subject to severe industrial pollution, an ecological floating bed was used to prevent and control water pollution. The river flows through multiple industrial zones, and the water quality and types of pollutants vary with the seasons and factory production cycles. To adapt to these changes, an adjustable multi-layer ecological floating bed system was designed. However, as the floating bed system operated for extended periods, the growth of bottom-layer plants was affected by shading from the upper layers, resulting in reduced purification efficiency. To address this issue, the floating bed was designed to periodically flip, ensuring that all plants receive adequate sunlight. However, this flipping mechanism temporarily disturbs the water, impacting the habitat of aquatic life. Furthermore, due to seasonal fluctuations in the river's water level, the floating bed system also needed to be able to automatically adjust its height as the water level rose and fell.

[0003] The existing ecological floating bed system has the following shortcomings when facing complex water pollution environments:

[0004] The system lacks the ability to respond to changes in water quality in real time, and cannot adjust purification strategies in a timely manner according to the dynamic changes in pollutant concentrations. This results in the system being unable to quickly adapt and maintain efficient purification effects when faced with sudden pollution or seasonal changes.

[0005] While the multi-layer floating bed structure increases the total number of plants per unit area, insufficient light for the lower layers seriously affects purification efficiency. Long-term insufficient light can cause the lower layer plants to grow slowly or even wither, significantly reducing the purification capacity of the entire system.

[0006] While the floating bed's flipping mechanism can address uneven lighting, frequent flipping can disturb the water and impact the stability of the aquatic ecosystem. Excessive disturbance can lead to an increase in suspended solids, affecting water quality, disrupting the normal life of aquatic animals, and upsetting the ecological balance.

[0007] Existing systems lack the ability to automatically adapt to water level fluctuations, making it difficult to ensure optimal contact between plant roots and the water. Water level fluctuations affect the contact area between plant roots and the water, thereby affecting purification efficiency. Failure to adjust the floating bed height in a timely manner can result in plant roots being exposed to air or completely submerged, both of which reduce purification effectiveness.

[0008] Lacking an intelligent control strategy that comprehensively considers water quality, plant growth, and ecological impacts, it is difficult to strike a balance between purification efficiency and ecological protection. Existing systems often only focus on a single factor, such as water quality purification efficiency, while ignoring the impact on the entire ecosystem. This single optimization strategy may improve purification efficiency in the short term, but in the long run, it may disrupt the ecological balance of the water body.

[0009] In addition, existing systems usually adopt fixed operating parameters, such as fixed flipping frequencies and speeds. This rigid control method cannot adapt to the complex and changing water environment. The water quality conditions and biological activity patterns vary in different seasons and at different time periods, and fixed operating parameters are difficult to achieve the best results in all situations.

[0010] Meanwhile, existing systems lack a real-time monitoring and feedback mechanism for the growth status of plants. The growth status of plants directly affects purification efficiency, but existing systems often ignore this point and cannot adjust the operating strategy according to the actual growth of plants.

[0011] Finally, existing systems perform poorly in dealing with complex multiple pollutants. Different types of pollutants may require different treatment methods and plant species, but existing systems often adopt a single plant configuration and are difficult to cope with complex pollution situations.

[0012] In view of the above problems, the existing technology urgently needs to be improved. Summary of the Invention

[0013] The purpose of this application is to provide an intelligent water pollution prevention and control system and method based on an ecological floating bed, aiming to solve at least one problem existing in the prior art, and having the advantages of real-time response to water quality changes, optimizing the multi-layer floating bed structure, intelligent control of floating bed flipping, automatic adaptation to water level changes, and balancing purification efficiency and ecological protection.

[0014] This application provides a water pollution prevention and control method based on an ecological floating bed, and the technical solution is as follows:

[0015] The steps of this method include: obtaining water quality parameters, plant growth status, and water level change information; calculating the water quality purification efficiency according to the water quality parameters, plant growth status, and water level change information; determining the floating bed flipping frequency, flipping speed, and floating bed height according to the difference between the water quality purification efficiency and the preset target efficiency; controlling the floating bed to perform the flipping action of the flipping frequency and flipping speed and the adjustment action of the floating bed height to improve the water quality purification efficiency and minimize the interference to the aquatic ecosystem.

[0016] Furthermore, the present application also proposes that the steps of controlling the floating bed to perform the flipping actions of the flipping frequency and flipping speed and the adjustment action of the floating bed height to improve the water quality purification efficiency and minimize the interference to the aquatic ecosystem include: obtaining water quality parameters within a preset monitoring period; calculating the change rate of the water quality purification efficiency based on the water quality parameters to obtain the water quality change trend; when the water quality change trend shows that the water quality purification efficiency continuously decreases, starting an aquatic biological activity monitoring device to obtain aquatic biological activity intensity data; determining the execution time period with the lowest aquatic biological activity intensity within a preset time range according to the aquatic biological activity intensity data; within the execution time period: obtaining a preset control parameter adjustment step size; sequentially adjusting the flipping frequency, flipping speed, and floating bed height according to the step size; obtaining the adjusted water quality purification efficiency; when the water quality purification efficiency reaches the preset target efficiency, keeping the current control parameters unchanged.

[0017] Furthermore, the present application also proposes that the steps of, when the water quality change trend shows that the water quality purification efficiency continuously decreases, starting an aquatic biological activity monitoring device to obtain aquatic biological activity intensity data include: obtaining historical data of the water quality purification efficiency within a preset monitoring period; obtaining a preset change rate threshold of the water quality purification efficiency; calculating the ratio of the difference in water quality purification efficiency between adjacent time points to the time interval based on the historical data to obtain the water quality purification efficiency change rate; when the change rate is negative in three consecutive monitoring periods and its absolute value exceeds the change rate threshold, determining that the water quality purification efficiency continuously decreases; determining the sampling frequency of the aquatic biological activity monitoring device according to the corresponding relationship between the absolute value of the change rate and the preset sampling frequency, where the larger the absolute value of the change rate, the higher the sampling frequency; collecting aquatic biological activity intensity data according to the sampling frequency.

[0018] Furthermore, the present application also proposes that the steps of determining the execution time period with the lowest aquatic biological activity intensity within a preset time range according to the aquatic biological activity intensity data include: obtaining a preset monitoring time range and a time window division parameter N, where N is an integer greater than 1; within the monitoring time range, obtaining aquatic biological activity intensity data at multiple time points according to a preset sampling interval; equally dividing the monitoring time range into N consecutive time windows according to the time window division parameter N; calculating the arithmetic mean of all aquatic biological activity intensity data within each time window; comparing the averages of the N time windows and determining the time window with the lowest average as the execution time period.

[0019] Furthermore, the present application also proposes that the step of determining the floating bed flipping frequency, flipping speed, and floating bed height according to the difference between the water purification efficiency and the preset target efficiency includes: obtaining a preset water quality parameter monitoring period; obtaining a water purification efficiency adjustment coefficient and a control parameter adjustment step size within the monitoring period; obtaining a preset priority order of control parameters, where the floating bed flipping frequency has the highest priority, followed by the flipping speed, and the floating bed height has the lowest priority; calculating the difference between the water purification efficiency and the preset target efficiency; multiplying the difference by the water purification efficiency adjustment coefficient to obtain the total control parameter adjustment amount; dividing the total control parameter adjustment amount by the control parameter adjustment step size to obtain the number of adjustments; adjusting the floating bed flipping frequency, flipping speed, and floating bed height in sequence within their respective preset adjustment ranges according to the priority order, where: when adjusting the floating bed flipping frequency, the adjustment amplitude each time is the control parameter adjustment step size and does not exceed the preset maximum flipping frequency; when adjusting the flipping speed, the adjustment amplitude each time is the control parameter adjustment step size and does not exceed the preset maximum flipping speed; when adjusting the floating bed height, the adjustment amplitude each time is the control parameter adjustment step size and does not exceed the preset maximum adjustment height.

[0020] Furthermore, the present application also proposes that the step of obtaining the water purification efficiency adjustment coefficient and the control parameter adjustment step size within the monitoring period includes: obtaining preset historical data collection parameters, including the number of historical monitoring periods N and the reference adjustment coefficient; continuously obtaining the water purification efficiency data of N historical monitoring periods, where N is an integer greater than or equal to 10; calculating the sum of squared deviations of the water purification efficiency data within the N historical monitoring periods and dividing it by N - 1 to obtain the variance; performing a square root operation on the variance to obtain the standard deviation; multiplying the standard deviation by the reference adjustment coefficient to obtain the water purification efficiency adjustment coefficient; obtaining a preset minimum number of adjustments and a maximum number of adjustments; within the N historical monitoring periods, calculating the absolute value of the difference in water purification efficiency between adjacent two monitoring periods, and selecting the maximum value as the maximum change amount; dividing the maximum change amount by the maximum number of adjustments to obtain the control parameter adjustment step size.

[0021] Further, the present application also proposes that the method further includes: obtaining concentration data and type information of various pollutants in the water body from a preset water quality monitoring system; obtaining a corresponding plant configuration plan from a preset plant database according to the concentration data and type information of the pollutants; dividing the floating bed into an upper layer, a middle layer and a bottom layer, and obtaining data on light intensity, temperature and dissolved oxygen of each layer; configuring first-class plants for absorbing nitrogen and phosphorus in the upper layer according to the plant configuration plan and the light intensity, temperature and dissolved oxygen data of the upper layer; configuring second-class plants for adsorbing heavy metals in the middle layer according to the plant configuration plan and the light intensity, temperature and dissolved oxygen data of the middle layer; configuring third-class plants tolerant to low light in the bottom layer according to the plant configuration plan and the light intensity, temperature and dissolved oxygen data of the bottom layer; monitoring the rhizosphere environment parameters of the first-class plants, the second-class plants and the third-class plants; regulating the physical and chemical properties of the culture medium according to the rhizosphere environment parameters to promote the formation of a microbial community; and realizing the purification of the pollutants through the synergistic action of the root exudates of the first-class plants, the second-class plants and the third-class plants and the microbial community.

[0022] Further, the present application also proposes that the step of regulating the physical and chemical properties of the culture medium according to the rhizosphere environment parameters to promote the formation of a microbial community includes: obtaining the rhizosphere environment parameters of the first-class plants, the second-class plants and the third-class plants, where the rhizosphere environment parameters include pH value, redox potential and organic matter content; obtaining the target parameter ranges required for the growth of the microbial community from a preset parameter database, including the target pH value range, the target redox potential range and the target organic matter content range; selecting an adapted microbial strain from a preset microbial strain database according to the target parameter ranges; monitoring the rhizosphere environment parameters at a preset detection time interval; when the detected rhizosphere environment parameters are not within the corresponding target ranges, selecting a corresponding regulator from a preset regulator database, where the regulators include: a buffer for regulating the pH value, a potential regulator for regulating the redox potential, and an organic nutrient for supplementing organic matter; adding the selected regulator to the culture medium according to a preset addition amount; continuing to monitor the rhizosphere environment parameters at the preset detection time interval until the rhizosphere environment parameters reach and stabilize within the corresponding target ranges; and inoculating the adapted microbial strain into the culture medium to promote the formation of a microbial community.

[0023] Furthermore, the present application also proposes that the step of calculating the water quality purification efficiency according to the water quality parameters, plant growth conditions, and water level change information specifically includes: taking the pollutant concentration in the water quality parameters as P(t), the plant coverage area in the plant growth conditions as C(t), and the water level change information as H(t), and substituting them into the following mathematical model to calculate the water quality purification efficiency: E(t) = k1 * [1 - exp(-α * T(t))] * [P(t) / Pmax] * [1 - β * D(t)] * [C(t) / Cref] * [1 + γ * sin(2π * t / 24)] * [1 + λ * (H(t) - Href) / Href] * exp[-μ * (|ΔR(t)| / Rmax)] where E(t) represents the purification efficiency of the system at time t, and its value range is 0 - 1; T(t) represents the system operation time, with the unit of hour; P(t) represents the current pollutant concentration, with the unit of mg / L; Pmax represents the maximum treatable pollutant concentration, with the unit of mg / L; D(t) represents the water body disturbance degree, and its value range is 0 - 1; C(t) represents the plant coverage, with the unit of m 2 ; Cref represents the reference plant coverage, with the unit of m 2 ; H(t) represents the current water level, with the unit of m; Href represents the reference water level, with the unit of m; ΔR(t) represents the flip angle change rate, with the unit of degree / hour; Rmax represents the maximum allowable flip angle, with the unit of degree; k1 represents the basic purification efficiency coefficient, and its value range is 0.8 - 0.95; α represents the time decay coefficient, and its value range is 0.01 - 0.05; β represents the disturbance influence coefficient, and its value range is 0.1 - 0.3; γ represents the light cycle influence coefficient, and its value range is 0.1 - 0.2; λ represents the water level influence coefficient, and its value range is 0.2 - 0.4; μ represents the flip disturbance decay coefficient, and its value range is 0.1 - 0.3.

[0024] Furthermore, the present application also proposes a water pollution prevention and control system based on an ecological floating bed. The system includes: a data acquisition module for obtaining water quality parameters, plant growth conditions, and water level change information; an efficiency calculation module for calculating the water quality purification efficiency according to the water quality parameters, plant growth conditions, and water level change information; a parameter determination module for determining the floating bed flip frequency, flip speed, and floating bed height according to the difference between the water quality purification efficiency and the preset target efficiency; and an execution control module for controlling the floating bed to perform the flip action of the flip frequency and flip speed and the adjustment action of the floating bed height to improve the water quality purification efficiency and minimize the interference to the aquatic ecosystem.

[0025] As can be seen from the above, an intelligent water pollution prevention and control system and method based on an ecological floating bed provided by the present application obtain water quality parameters, plant growth conditions, and water level change information, calculate the water quality purification efficiency, and dynamically adjust the flipping frequency, flipping speed, and height of the floating bed according to the difference between the purification efficiency and the target efficiency, thereby achieving real-time response to water quality changes, optimization of the multi-layer floating bed structure, intelligent control of floating bed flipping, automatic adaptation to water level changes, and balance between purification efficiency and ecological protection. It has the advantages of real-time response to water quality changes, optimization of the multi-layer floating bed structure, intelligent control of floating bed flipping, automatic adaptation to water level changes, and balance between purification efficiency and ecological protection. Description of the Drawings

[0026] Figure 1 It is a flowchart of an intelligent water pollution prevention and control method based on an ecological floating bed provided by the present application.

[0027] Figure 2 It is a schematic diagram of an intelligent water pollution prevention and control system based on an ecological floating bed provided by the present application.

[0028] In the figure: 210, data acquisition module; 220, efficiency calculation module; 230, parameter determination module; 240, execution control module. Detailed Embodiments

[0029] Next, the technical solutions in the present application will be clearly and completely described in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0030] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0031] When applying multi - layer ecological floating beds for water pollution prevention and control, the system faces complex technical challenges. The primary problem is the lack of real - time response ability to water quality changes, and the purification strategy cannot be adjusted in a timely manner according to the dynamic changes of pollutant concentrations. Secondly, although the multi - layer structure increases the total amount of plants per unit area, insufficient light for the lower - layer plants seriously affects the purification efficiency. In addition, although the floating bed flipping mechanism can solve the problem of uneven light, frequent flipping will cause disturbances to the water body and affect the stability of the aquatic ecosystem. At the same time, the system lacks the ability to automatically adapt to water - level changes and it is difficult to ensure the optimal contact state between the plant roots and the water body. Most critically, the existing system lacks an intelligent control strategy that comprehensively considers water quality, plant growth, and ecological impacts, and it is difficult to achieve a balance between purification efficiency and ecological protection. These problems directly affect the water - quality purification efficiency, plant growth conditions, and the health of the aquatic ecosystem, restricting the application effect of ecological floating - bed technology in complex water environments.

[0032] In a typical industrial - pollution river section, a set of multi - layer ecological floating - bed system was deployed for water - quality purification. The water - quality parameters in this river section show highly dynamic changes, and the fluctuation range of indicators such as COD, ammonia nitrogen, and total phosphorus can reach 30% within 24 hours. The floating - bed system consists of upper, middle, and lower layers, with different types of aquatic plants planted in each layer. The system is equipped with on - line water - quality monitoring equipment, plant - growth - condition sensors, and water - level monitoring devices to collect relevant data in real - time. However, due to the lack of an intelligent control strategy, the system cannot automatically adjust the operating parameters according to these data. For example, when the chlorophyll content of the lower - layer plants drops by 20%, it indicates insufficient light, but the system cannot automatically increase the flipping frequency. At the same time, when the water level changes, the height of the floating bed fails to be adjusted accordingly, resulting in some plant roots being separated from the water body. In addition, when the dissolved - oxygen content in the water body is detected to decrease, the system fails to reduce the flipping speed to reduce the disturbance to the water body. These problems lead to the overall purification efficiency of the system being lower than the theoretical value, and the plant - growth conditions being uneven, resulting in a decline in the aquatic - biodiversity index.

[0033] If these technical problems cannot be effectively solved, a series of serious consequences will occur. First of all, the low water - quality purification efficiency will cause the pollutant accumulation rate to exceed the treatment rate, leading to a continuous deterioration of the water - body quality. Secondly, uneven plant growth will cause some plants to wither prematurely, increasing the system maintenance cost and replacement frequency. Moreover, the continuous interference with the aquatic ecosystem will reduce biodiversity, damage the water - body self - purification ability, and form a vicious cycle. In the long run, this will not only affect the effect of water - quality improvement, but also reduce the stability and recovery ability of the entire ecosystem. In addition, due to the low efficiency of the system, it may be necessary to increase the area of the floating bed to achieve the expected purification effect, which will occupy more water - surface space and affect other functions of the river channel. Therefore, there is an urgent need for an ecological floating - bed system that can be intelligently regulated to adapt to complex and changeable water environments, while improving the water - quality purification efficiency and maximizing the protection of the aquatic ecosystem.

[0034] After analyzing the problems existing in the existing ecological floating bed system, this application began to explore possible solutions. First, the method of single-parameter adjustment was considered, such as adjusting the floating bed height only according to the water quality change, or adjusting the flipping frequency only based on the plant growth condition. However, this method cannot comprehensively cope with the complex water environment changes.

[0035] Then, the scheme of multi-parameter independent adjustment was considered, that is, adjusting different control parameters according to the water quality, plant growth and water level changes respectively. But this method may lead to conflicts among various adjustments and it is difficult to achieve the best balance.

[0036] After in-depth thinking, this application proposed an intelligent control method that comprehensively considers various factors. Specifically, this method first obtains the water quality parameters, plant growth conditions and water level change information, which comprehensively reflects the water environment and the operation status of the floating bed system. Then, based on this information, the water quality purification efficiency is calculated, which is used as the key index of the system performance.

[0037] Next, this application innovatively proposed a method to determine the control parameters according to the difference between the water quality purification efficiency and the preset target efficiency. Among them, the control parameters include the floating bed flipping frequency, flipping speed and floating bed height. This method can dynamically adjust the system operation parameters according to the actual purification effect, realizing the adaptive control of the complex environment.

[0038] Finally, this application designed the execution control steps, and by precisely controlling the flipping action and height adjustment of the floating bed, while improving the water quality purification efficiency, minimizing the interference to the aquatic ecosystem.

[0039] Thus, with reference to Figure 1 , this application proposed a water pollution prevention method based on an ecological floating bed, and the steps of this method include:

[0040] S110. Obtain the water quality parameters, plant growth conditions and water level change information;

[0041] S120. Calculate the water quality purification efficiency according to the water quality parameters, plant growth conditions and water level change information;

[0042] S130. Determine the floating bed flipping frequency, flipping speed and floating bed height according to the difference between the water quality purification efficiency and the preset target efficiency;

[0043] S140. Control the floating bed to execute the flipping action with the flipping frequency and flipping speed and the adjustment action of the floating bed height to improve the water quality purification efficiency and minimize the interference to the aquatic ecosystem.

[0044] Among them, water quality parameters refer to various indicators reflecting the degree of water body pollution, and specifically can include indicators such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), total nitrogen, total phosphorus, dissolved oxygen, etc.

[0045] Among them, the plant growth condition refers to various indicators reflecting the health degree of plants on the floating bed, and specifically can be realized by using indicators such as chlorophyll content, plant height, root development degree, biomass, etc.

[0046] Among them, the water level change information refers to the data reflecting the change of water body depth, and specifically can be realized by using an ultrasonic water level sensor or a pressure type water level gauge.

[0047] Among them, the water quality purification efficiency refers to the degree of pollutants removed per unit time, and specifically can be calculated by using a preset mathematical model.

[0048] Among them, the floating bed flipping frequency refers to the number of times the floating bed completes flipping per unit time, and specifically can be realized by using the number of flips per hour.

[0049] Among them, the flipping speed refers to the time required for the floating bed to complete one flip, and specifically can be realized by using the angular velocity in degrees per second.

[0050] Among them, the floating bed height refers to the vertical distance from the bottom of the floating bed to the water surface, and specifically can be realized by using an adjustable support structure.

[0051] The core innovation point of this application is to propose a method. This method calculates the water quality purification efficiency by obtaining water quality parameters, plant growth conditions, and water level change information, and dynamically adjusts the flipping frequency, flipping speed, and height of the floating bed according to the difference between the purification efficiency and the target efficiency. This method can achieve adaptive control of complex water environments, minimizing interference with the aquatic ecosystem while improving the water quality purification efficiency.

[0052] The working principle of this application can be described in detail as follows: First, water quality parameters, including indicators such as COD, BOD, total nitrogen, and total phosphorus, are obtained through water quality on-line monitoring equipment. At the same time, data such as chlorophyll content and plant height are collected by using plant growth condition sensors, and the water level change information is recorded through a water level monitoring device. These data are transmitted to the central control system in real time.

[0053] Next, according to the obtained water quality parameters, plant growth conditions, and water level change information, the current water quality purification efficiency is calculated by using a preset mathematical model. This model takes into account the influence of factors such as pollutant concentration, plant coverage area, and water level on the purification efficiency.

[0054] Then, compare the calculated water purification efficiency with the preset target efficiency to obtain the difference. Based on this difference, the system uses an optimization algorithm to determine the floating bed flipping frequency, flipping speed, and floating bed height that need to be adjusted. The key to this step is to find the parameter combination that can maximize the purification efficiency while minimizing the interference to the aquatic ecosystem.

[0055] Finally, send instructions to the mechanical control system of the floating bed according to the determined parameters to achieve the flipping and height adjustment of the floating bed. The flipping action ensures that each layer of plants can receive uniform light, and the height adjustment ensures that the plant roots maintain the best contact state with the water body.

[0056] The advantage of this application is that it can dynamically adjust the system operation parameters according to real-time environmental changes, avoiding the problem of low efficiency caused by fixed parameters. At the same time, by comprehensively considering multiple factors, the system can achieve a balance between purification efficiency and ecological protection, realizing intelligent and refined control.

[0057] As a preferred implementation method, this application can be implemented in an industrial-polluted river section. For example, the length of this river section is 500 meters, the width is 30 meters, and the average water depth is 3 meters. The floating bed system consists of 10 independent rectangular floating bed units, each unit with a size of 10 meters × 5 meters, and the total coverage area is 500 square meters, accounting for 33.3% of the river section area.

[0058] The floating bed adopts a three-layer structure design, with each layer having a height of 30 cm and an interlayer distance of 20 cm. Floating plants such as water hyacinths and water spinach are planted in the upper layer, emergent plants such as reeds and cattails are planted in the middle layer, and submerged plants such as Myriophyllum verticillatum and Hydrilla verticillata are planted in the lower layer. The floating bed frame is made of high-density polyethylene material, with good corrosion resistance and durability.

[0059] The water quality on-line monitoring equipment collects water samples every 15 minutes and analyzes indicators such as COD, total nitrogen, and total phosphorus. The plant growth status sensors include a chlorophyll fluorometer and an image analysis system, which scan the plant growth situation once an hour. The water level monitoring device uses an ultrasonic water level gauge with an accuracy of ±1 cm and records the water level data every 5 minutes.

[0060] The flipping mechanism of the floating bed adopts an electric gear drive system, which can achieve a flipping angle of 0 - 180 degrees, and the flipping speed can be adjusted between 0.1 - 1 degree / second. The height adjustment mechanism is controlled by a hydraulic cylinder, with an adjustment range of 0 - 100 cm and an accuracy of ±1 cm.

[0061] The central control system uses an industrial-grade computer and runs specially developed intelligent control software. This software is based on machine learning algorithms and can predict the optimal control strategy according to historical data and current environmental parameters. The system performs parameter optimization calculations every 30 minutes and adjusts the floating bed operation parameters according to the calculation results.

[0062] In some specific embodiments, when it is detected that the COD concentration suddenly rises by 20%, the system will increase the floating bed flipping frequency from 2 times a day to 3 times a day, and at the same time reduce the flipping speed from 0.5 degrees per second to 0.3 degrees per second to increase the contact time between the plants and the water body. When the water level rises by 30 cm, the system will correspondingly raise the floating bed by 20 cm to ensure that the plant roots are always in the best absorption position.

[0063] In some of the above embodiments, during the implementation of the present application, there is also a problem of how to precisely control the flipping of the floating bed and the height adjustment to minimize the interference to the aquatic ecosystem when the water purification efficiency decreases.

[0064] In response to this, the present application further proposes that when the method controls the flipping action of the floating bed for the flipping frequency and flipping speed and the adjustment action of the floating bed height, a series of refined control strategies are adopted.

[0065] The method first obtains the water quality parameters within a preset monitoring period, and calculates the change rate of the water purification efficiency based on these parameters to obtain the water quality change trend. When the water quality change trend shows that the water purification efficiency continuously decreases, the aquatic biological activity monitoring device is started to obtain the aquatic biological activity intensity data. The purpose of this step is to minimize the impact on the aquatic ecosystem when adjusting the floating bed parameters.

[0066] Next, according to the obtained aquatic biological activity intensity data, the execution time period with the lowest aquatic biological activity intensity within a preset time range is determined. This step is designed considering the activity rules of aquatic organisms. Selecting the time period when the biological activity is least active for floating bed adjustment can minimize the interference to the ecosystem.

[0067] Within the determined execution time period, the method adopts a progressive adjustment strategy. First, the preset control parameter adjustment step size is obtained, and then the flipping frequency, flipping speed, and floating bed height are adjusted in sequence according to this step size. After each adjustment, the adjusted water purification efficiency is obtained. When the water purification efficiency reaches the preset target efficiency, the current control parameters are kept unchanged. This progressive adjustment method can avoid violent disturbance of the water body caused by excessive parameter adjustment.

[0068] A key feature of this method is the introduction of an aquatic biological activity monitoring device. This device can be an underwater acoustic sensor, an optical sensor, a bioelectric signal detector, etc. Through these sensors, the activities of organisms in the water can be monitored in real time, such as the swimming frequency of fish and the density change of plankton. These data provide a scientific basis for selecting the best execution time period.

[0069] Another important feature is the setting of the control parameter adjustment step size. This step size is not fixed but can be dynamically adjusted according to the severity of water quality changes. For example, when the water quality deteriorates rapidly, the step size can be appropriately increased to speed up the adjustment; when the water quality changes slowly, the step size can be decreased to achieve more precise control.

[0070] In practical applications, this method can be used in combination with an intelligent control system. For example, a prediction model based on machine learning algorithms can be set up to predict the future water quality change trend and the activity rules of aquatic organisms according to historical data. This can advance the planning of the floating bed adjustment strategy and further optimize the selection of the execution time.

[0071] Specifically, when it is detected that the water quality purification efficiency continuously decreases, the system will first analyze the historical data to determine the daily cycle pattern of aquatic organism activities. Suppose it is found that the activity intensity of aquatic organisms is the lowest between 2 am and 4 am. The system will set this time period as the execution time period. During this time period, the system will adjust the parameters step by step according to the preset step size, such as adjusting the flipping frequency by 0.1 times per hour, the flipping speed by 0.5 degrees per second, and the floating bed height by 1 cm each time. After each adjustment, the system will wait for a preset stable time, such as 30 minutes, and then re-detect the water quality purification efficiency. If the efficiency improves but has not reached the target, the system will continue to adjust; if the efficiency reaches the target, the system will stop adjusting and record the current parameters.

[0072] Through this refined control strategy, this method can improve the water quality purification efficiency while minimizing the interference to the aquatic ecosystem. Compared with the traditional fixed-parameter control, this method can better adapt to the complex and changeable water environment and achieve the balance between water quality purification and ecological protection.

[0073] In addition, this method can also be integrated with other water quality monitoring and ecological assessment systems. For example, water quality on-line monitoring devices can be introduced to obtain parameters such as dissolved oxygen, pH value, and turbidity in real time as supplementary bases for adjusting the floating bed parameters. At the same time, regular surveys on aquatic biodiversity can be carried out to evaluate the long-term impact of the floating bed system on the overall ecological environment and optimize the control strategy accordingly.

[0074] Generally speaking, the method proposed in this application effectively solves the problem of how to precisely control the flipping and height adjustment of the floating bed to minimize the interference to the aquatic ecosystem when the water quality purification efficiency decreases by introducing innovations such as monitoring the activities of aquatic organisms, selecting the best execution time period, and adopting a progressive adjustment strategy. This method not only improves the efficiency of water quality purification but also fully considers the needs of ecological protection, providing a more intelligent and environmentally friendly solution for pollution prevention and control in complex water environments.

[0075] In some of the above embodiments, during the implementation of the present application, there is still a problem of how to accurately judge the continuous decline of the water purification efficiency and timely start the monitoring of aquatic biological activities.

[0076] In response to this, the present application further proposes that when the water quality change trend shows that the water purification efficiency continuously decreases, the aquatic biological activity monitoring device is started to obtain the aquatic biological activity intensity data.

[0077] The technical solution of the present application obtains the historical data of the water purification efficiency within a preset monitoring period, sets a threshold for the change rate of the water purification efficiency, calculates the ratio of the difference in the water purification efficiency between adjacent time points to the time interval, and obtains the change rate of the water purification efficiency. When the change rate is negative in three consecutive monitoring periods and its absolute value exceeds the change rate threshold, it is determined that the water purification efficiency continuously decreases. Subsequently, according to the corresponding relationship between the absolute value of the change rate and the preset sampling frequency, the sampling frequency of the aquatic biological activity monitoring device is determined, where the larger the absolute value of the change rate, the higher the sampling frequency. Finally, the aquatic biological activity intensity data is collected according to the determined sampling frequency.

[0078] By introducing the concept of the change rate of the water purification efficiency and setting the change rate threshold, the technical solution of the present application can more accurately judge whether the water purification efficiency continuously decreases. Through the judgment of three consecutive monitoring periods, misjudgment caused by short-term fluctuations can be effectively avoided, improving the accuracy and reliability of the judgment.

[0079] In addition, the technical solution of the present application also introduces the corresponding relationship between the sampling frequency and the absolute value of the change rate, realizing the dynamic adjustment of the sampling frequency. When the water purification efficiency decreases rapidly, a higher sampling frequency is adopted to capture the changes in aquatic biological activities more timely; when the decrease rate is slow, a lower sampling frequency is adopted to reduce unnecessary energy consumption and data processing burden.

[0080] Specifically, the following steps can be adopted:

[0081] First, set the preset monitoring period to 24 hours and the threshold for the change rate of the water purification efficiency to 0.05.

[0082] Then, obtain the historical data of the water purification efficiency for 72 consecutive hours (i.e., 3 monitoring periods). Assume that the water purification efficiency is recorded every 8 hours within these 72 hours, and the obtained data are: 0.85, 0.83, 0.80, 0.78, 0.75, 0.71, 0.68, 0.64, 0.61.

[0083] Next, calculate the ratio of the difference in the water purification efficiency between adjacent time points to the time interval to obtain the change rate of the water purification efficiency:

[0084] The first cycle: (-0.02 / 8, -0.03 / 8, -0.02 / 8) = (-0.0025, -0.00375, -0.0025)

[0085] The second cycle: (-0.03 / 8, -0.04 / 8, -0.03 / 8) = (-0.00375, -0.005, -0.00375)

[0086] The third cycle: (-0.03 / 8, -0.04 / 8, -0.03 / 8) = (-0.00375, -0.005, -0.00375)

[0087] It can be seen that the change rates are all negative in three consecutive monitoring cycles, and the absolute value of -0.005 in the second and third cycles exceeds the set change rate threshold of 0.05. Therefore, it is determined that the water purification efficiency continuously decreases.

[0088] Set the sampling frequency according to the corresponding relationship between the absolute value of the change rate and the preset sampling frequency. For example, the following corresponding relationship can be formulated:

[0089] The absolute value of the change rate < 0.003, and the sampling frequency is once every 4 hours

[0090] 0.003 ≤ the absolute value of the change rate < 0.005, and the sampling frequency is once every 2 hours

[0091] The absolute value of the change rate ≥ 0.005, and the sampling frequency is once every 1 hour

[0092] In this example, the maximum absolute value of the change rate is 0.005. Therefore, the sampling frequency of the aquatic biological activity monitoring device is determined to be once every 1 hour.

[0093] Finally, collect the data of the aquatic biological activity intensity according to the determined sampling frequency (once every 1 hour). These data can include indicators such as the movement frequency and activity range of aquatic organisms, and are used for subsequent analysis and decision-making.

[0094] Through the above embodiments, the technical solution of the present application can accurately judge the continuous decline trend of the water purification efficiency, and dynamically adjust the sampling frequency of the aquatic biological activity monitoring according to the decline speed. This method not only improves the accuracy and timeliness of the monitoring, but also optimizes the resource utilization while ensuring the monitoring effect, providing reliable data support for the subsequent adjustment of the floating bed control strategy.

[0095] Compared with the prior art, the technical solution of the present application introduces the concept of the change rate of water purification efficiency, and improves the accuracy of judging the downward trend of water purification efficiency through continuous judgments in multiple cycles. By adopting a dynamically adjusted sampling frequency, while ensuring the monitoring effect, the use of energy and computing resources is optimized. Through accurate judgment of the decline in water purification efficiency and timely monitoring of aquatic biological activities, more reliable and timely data support is provided for subsequent adjustment of the floating bed control strategy, which helps to achieve a better balance between improving water purification efficiency and protecting the aquatic ecosystem.

[0096] In some of the above embodiments, during the implementation of the present application, there is also a problem of how to accurately determine the execution time period with the lowest intensity of aquatic biological activities.

[0097] In response to this, the present application further proposes that specific steps are adopted when the method determines the execution time period with the lowest intensity of aquatic biological activities within a preset time range.

[0098] The method first obtains a preset monitoring time range and a time window division parameter N, where N is an integer greater than 1. This can flexibly set the monitoring range and the number of time windows to adapt to different application scenarios.

[0099] Within the set monitoring time range, aquatic biological activity intensity data at multiple time points are obtained according to a preset sampling interval. This way can ensure the continuity and representativeness of the data, providing a reliable basis for subsequent analysis.

[0100] According to the time window division parameter N, the monitoring time range is equally divided into N consecutive time windows. This division method can ensure that the length of each time window is equal, facilitating subsequent comparative analysis.

[0101] Next, the arithmetic mean of all aquatic biological activity intensity data within each time window is calculated. By calculating the mean value, the influence of fluctuations of individual data points can be eliminated, and a more stable and reliable activity intensity index can be obtained.

[0102] Finally, the means of the N time windows are compared, and the time window with the lowest mean value is determined as the execution time period. This method can effectively identify the time period with the overall lowest intensity of aquatic biological activities, thereby minimizing the interference of floating bed flipping on the aquatic ecosystem to the greatest extent.

[0103] By dividing the continuous time range into multiple equal-length time windows and calculating the mean value of the aquatic biological activity intensity within each window, the method can effectively smooth the influence of short-term fluctuations and obtain a more reliable activity intensity evaluation result. By comparing the means of different time windows, the time period with the lowest intensity of aquatic biological activities can be accurately identified, providing the best execution timing for the floating bed flipping operation.

[0104] The advantage of this method is that it takes into account the data over a relatively long time range, rather than relying solely on the instantaneous data at a single time point. This can avoid misjudgments caused by accidental factors and improve the accuracy and reliability of the selection of the execution time period. At the same time, by setting the parameter N, the size of the time window can be flexibly adjusted to adapt to the differences in the activity patterns of aquatic organisms in different water environments.

[0105] In specific implementation, 24 hours can be selected as the monitoring time range, and N can be set to 6, that is, one day is divided into 6 time windows of 4 hours each. The sampling interval can be set to 15 minutes, so that each time window will contain 16 data points. Suppose the data of the activity intensity of aquatic organisms (represented after standardization) collected within a certain time window are: 2.1, 2.3, 2.0, 1.9, 2.2, 2.4, 2.1, 2.0, 1.8, 1.7, 1.9, 2.1, 2.3, 2.2, 2.0, 1.8. The average activity intensity of this time window is calculated to be 2.05. Calculate the average activity intensities of the other 5 time windows in the same way. Suppose the obtained results are 2.15, 2.30, 2.25, 2.10, and 2.20 respectively. By comparison, it can be determined that the time window with an average activity intensity of 2.05 is the execution time period with the lowest activity intensity of aquatic organisms.

[0106] Compared with the prior art, the method proposed in this application has the following advantages: First, by introducing the concept of a time window, the activity patterns of aquatic organisms can be evaluated more comprehensively, avoiding misjudgments that may be caused by relying solely on single-point data. Second, by calculating the average value within the time window, the influence of data fluctuations on the judgment is effectively reduced, improving the stability and reliability of the results. Finally, by flexibly setting the time window division parameters, this method can adapt to the activity characteristics of different water environments and different types of aquatic organisms, and has strong versatility and adaptability.

[0107] In some of the above embodiments, during the implementation of this application, there is also a problem of how to accurately adjust the floating bed flipping frequency, flipping speed, and floating bed height according to the difference between the water purification efficiency and the preset target efficiency.

[0108] In response to this, the present application further proposes that the method includes obtaining a preset water quality parameter monitoring period; obtaining a water quality purification efficiency adjustment coefficient and a control parameter adjustment step size within the monitoring period; obtaining a preset priority order of control parameters, where the floating bed flipping frequency has the highest priority, followed by the flipping speed, and the floating bed height has the lowest priority; calculating the difference between the water quality purification efficiency and the preset target efficiency; multiplying the difference by the water quality purification efficiency adjustment coefficient to obtain the total control parameter adjustment amount; dividing the total control parameter adjustment amount by the control parameter adjustment step size to obtain the number of adjustments; and sequentially adjusting the floating bed flipping frequency, flipping speed, and floating bed height within their respective preset adjustment ranges according to the priority order.

[0109] The technical solution of the present application realizes precise adjustment of the floating bed flipping frequency, flipping speed, and floating bed height by introducing a water quality purification efficiency adjustment coefficient and a control parameter adjustment step size. By setting the priority order of control parameters, the orderliness and efficiency of the adjustment process are ensured. This method can not only quickly respond to the real-time changes in water quality purification efficiency but also minimize the interference to the aquatic ecosystem while ensuring the adjustment effect.

[0110] Specifically, the technical solution of the present application first obtains a preset water quality parameter monitoring period, and the setting of this period needs to consider the speed of water quality change and the timeliness of system response. Within the monitoring period, a water quality purification efficiency adjustment coefficient and a control parameter adjustment step size are obtained. The water quality purification efficiency adjustment coefficient is used to convert the efficiency difference into an actual adjustment amount, while the control parameter adjustment step size determines the accuracy of each adjustment.

[0111] Next, a preset priority order of control parameters is obtained. The present application sets the floating bed flipping frequency to have the highest priority because the flipping frequency has the most direct and significant impact on water quality purification efficiency. Followed by the flipping speed, which affects the contact time between plants and water. The floating bed height has the lowest priority because it is mainly used to adapt to water level changes and has a relatively small impact on the purification efficiency in the short term.

[0112] After calculating the difference between the water quality purification efficiency and the preset target efficiency, multiply the difference by the water quality purification efficiency adjustment coefficient to obtain the total control parameter adjustment amount. This step converts the efficiency difference into a specific adjustment amount, making the subsequent adjustment more precise. Divide the total control parameter adjustment amount by the control parameter adjustment step size to obtain the number of adjustments, which ensures the smoothness and controllability of the adjustment process.

[0113] During the adjustment process, this application adjusts the floating bed flipping frequency, flipping speed, and floating bed height in sequence according to the priority order within their respective preset adjustment ranges. When adjusting the floating bed flipping frequency, the adjustment amplitude each time is the control parameter adjustment step size and does not exceed the preset maximum flipping frequency. This method not only ensures the accuracy of the adjustment but also avoids the negative impacts that may be brought by over-adjustment. The same principle also applies to the adjustment of the flipping speed and the floating bed height.

[0114] As a preferred implementation, the water quality parameter monitoring period can be set to 4 hours, the water quality purification efficiency adjustment coefficient to 0.8, and the control parameter adjustment step size to 0.1. Suppose the current water quality purification efficiency is 60%, and the preset target efficiency is 80%, then the efficiency difference is 20%. Multiply the difference by the adjustment coefficient to get the total control parameter adjustment amount of 16%. Divide the total adjustment amount by the adjustment step size to get the number of adjustments as 1.6, and round up to 2 times.

[0115] In this example, first, the floating bed flipping frequency is adjusted. Suppose the current flipping frequency is 1 time per hour, and the maximum flipping frequency is 3 times per hour. The first adjustment increases the flipping frequency by 0.1 times / hour to become 1.1 times / hour. After the second adjustment, the flipping frequency becomes 1.2 times / hour. If the target efficiency is not reached after adjusting the flipping frequency, then the flipping speed is adjusted, and finally, the floating bed height is adjusted until the preset target efficiency is reached or all parameters are adjusted to their respective maximum values.

[0116] Through this refined adjustment method, this application can minimize the interference to the aquatic ecosystem while ensuring the water quality purification efficiency. Compared with the prior art, the method of this application realizes the precise adjustment of control parameters by introducing the water quality purification efficiency adjustment coefficient and the control parameter adjustment step size, avoiding the problems of over-adjustment or under-adjustment. By setting the priority order of control parameters, the system can flexibly adjust each parameter according to the actual situation to achieve the best purification effect. This method can quickly respond to the real-time changes in the water quality purification efficiency, adapt to different water quality conditions and environmental changes. By precisely controlling the adjustment amplitude and frequency, it minimizes the interference to the aquatic ecosystem and achieves the balance between purification efficiency and ecological protection. This method can be easily extended to other types of water quality purification systems and has broad application prospects.

[0117] In some of the above embodiments, during the implementation of this application, there is also the problem of how to accurately obtain the water quality purification efficiency adjustment coefficient and the control parameter adjustment step size.

[0118] Regarding this, this application further proposes to obtain the water quality purification efficiency adjustment coefficient and the control parameter adjustment step size.

[0119] The technical solution of this application first obtains preset historical data collection parameters, including the number N of historical monitoring cycles and a reference adjustment coefficient. Then, it continuously obtains the water purification efficiency data for N historical monitoring cycles, where N is an integer greater than or equal to 10. Next, it calculates the sum of squared deviations of the water purification efficiency data within N historical monitoring cycles and divides it by N - 1 to obtain the variance. It performs a square root operation on this variance to obtain the standard deviation. The product of the standard deviation and the reference adjustment coefficient gives the water purification efficiency adjustment coefficient.

[0120] In addition, this application also obtains preset minimum and maximum adjustment times. Within N historical monitoring cycles, it calculates the absolute value of the difference in water purification efficiency between adjacent two monitoring cycles and selects its maximum value as the maximum change amount. It divides the maximum change amount by the maximum adjustment times to obtain the control parameter adjustment step size.

[0121] The technical solution of this application determines the water purification efficiency adjustment coefficient and the control parameter adjustment step size by analyzing historical data. This method has the following advantages:

[0122] First, by using data from multiple historical monitoring cycles, it can more comprehensively reflect the change trend of water purification efficiency and avoid the deviation that may be brought by a single data point. Selecting an integer N greater than or equal to 10 as the number of historical monitoring cycles can ensure that the data sample size is large enough to improve the reliability of statistical analysis.

[0123] Second, by adopting the calculation methods of variance and standard deviation, it can effectively quantify the dispersion degree of water purification efficiency data. The larger the standard deviation, the greater the fluctuation of water purification efficiency, and a larger adjustment coefficient is required to cope with this fluctuation. Multiplying the standard deviation by the reference adjustment coefficient can dynamically adjust the water purification efficiency adjustment coefficient according to the actual data fluctuation situation.

[0124] Third, by calculating the maximum value of the difference in water purification efficiency between adjacent monitoring cycles, it can capture the most extreme situation of water quality change. Dividing this maximum change amount by the maximum adjustment times, the obtained control parameter adjustment step size can cope with the maximum change and avoid overly drastic adjustment.

[0125] Specifically, the technical solution of this application can be implemented according to the following steps:

[0126] 1. Set historical data collection parameters: For example, N can be set to 12 (i.e., using data from the most recent 12 monitoring cycles), and the reference adjustment coefficient is set to 0.5.

[0127] 2. Collect historical data: Assume that the water purification efficiency data for 12 consecutive monitoring periods are: 0.65, 0.68, 0.72, 0.70, 0.69, 0.71, 0.73, 0.75, 0.74, 0.72, 0.71, 0.70.

[0128] 3. Calculate the variance and standard deviation:

[0129] Variance = [(0.65 - 0.7083)^2 +... + (0.70 - 0.7083)^2] / 11 = 0.000897

[0130] Standard deviation = √0.000897 ≈ 0.0299

[0131] 4. Calculate the water purification efficiency adjustment coefficient:

[0132] Adjustment coefficient = 0.0299 * 0.5 ≈ 0.015

[0133] 5. Determine the control parameter adjustment step:

[0134] Assume that the preset minimum adjustment times is 5 and the maximum adjustment times is 20.

[0135] Calculate the maximum efficiency difference between adjacent monitoring periods: max(|0.68 - 0.65|, |0.72 - 0.68|,..., |0.70 - 0.71|) = 0.03

[0136] Control parameter adjustment step = 0.03 / 20 = 0.0015

[0137] Through this method, the present application can dynamically adjust the control parameters according to the actual changes in water purification efficiency, thereby more precisely controlling the flipping frequency, flipping speed, and height of the floating bed. This adaptive control strategy can better respond to the dynamic changes in water quality and improve the response speed and regulation accuracy of the system.

[0138] As a preferred implementation, a weight coefficient can be further introduced to optimize the calculation of the water purification efficiency adjustment coefficient. For example, a higher weight can be assigned to the most recent monitoring period to better reflect the current water quality change trend. The specific implementation is as follows:

[0139] 1. Define the weight coefficient array W = [w1, w2,..., wN], where w1 > w2 >... > wN and Σwi = 1.

[0140] 2. Calculate the weighted average:

[0141] μw = Σ(wi * Ei) / N, where Ei is the water purification efficiency of the i-th monitoring period.

[0142] 3. Calculate the weighted variance:

[0143] σw^2 = Σ[wi * (Ei - μw)^2] / (N - 1)

[0144] 4. Calculate the weighted standard deviation:

[0145] σw = √σw^2

[0146] 5. Water quality purification efficiency adjustment coefficient = σw * reference adjustment coefficient

[0147] By introducing the weight coefficient, the technical solution of the present application can more sensitively respond to recent water quality changes, while not completely ignoring historical data, thus achieving a better balance between stability and responsiveness.

[0148] In addition, the present application can also dynamically adjust the reference adjustment coefficient according to seasonal changes or known water quality periodic change rules. For example, in seasons with relatively drastic water quality changes, the reference adjustment coefficient can be appropriately increased to enhance the adjustment ability of the system. Conversely, in seasons with relatively stable water quality, the reference adjustment coefficient can be decreased to reduce unnecessary adjustments, thereby saving energy and reducing interference with the water ecosystem.

[0149] Through these optimization measures, the technical solution of the present application can more intelligently and precisely control the ecological floating bed system, while improving the water quality purification efficiency, minimizing interference with the water ecosystem, and thus achieving a more efficient and environmentally friendly water pollution prevention effect.

[0150] In some of the above embodiments, during the implementation of the present application, there are also problems such as the concentration and type of various pollutants in the water body changing over time, and significant differences in light intensity, temperature, and dissolved oxygen conditions in different water layers.

[0151] In response to this, the present application further proposes that the method first obtains the concentration data and type information of various pollutants in the water body from a preset water quality monitoring system. Then, based on these data and information, it obtains the corresponding plant configuration plan from a preset plant database. Next, the floating bed is divided into upper, middle, and lower layers, and the light intensity, temperature, and dissolved oxygen data of each layer are obtained.

[0152] Based on the obtained data, the present application configures the first type of plants for absorbing nitrogen and phosphorus in the upper layer, the second type of plants for adsorbing heavy metals in the middle layer, and the third type of plants tolerant to weak light in the lower layer. This configuration method makes full use of the environmental characteristics of different water layers, enabling various plants to grow under the most suitable conditions.

[0153] To further improve the water quality purification effect, this application also monitors the rhizosphere environmental parameters of the first type of plants, the second type of plants, and the third type of plants. According to these parameters, the physical and chemical properties of the culture medium are regulated to promote the formation of the microbial community. Through the synergistic effect of plant root exudates and the microbial community, efficient purification of pollutants is achieved.

[0154] Specifically, the multi-layer ecological floating bed water quality purification method of this application can be achieved in the following ways:

[0155] First, the concentration data and type information of various pollutants in the water body are obtained in real time through the water quality monitoring system. For example, a multi-parameter water quality analyzer can be used to measure the concentrations of pollutants such as nitrogen, phosphorus, and heavy metals in the water body, and at the same time, methods such as spectral analysis are used to identify the pollutant types.

[0156] Next, based on the obtained pollutant data, the system will automatically screen out the most suitable plant configuration plan from the preset plant database. This database contains information such as the purification ability, growth characteristics, and environmental adaptability of various aquatic plants.

[0157] Then, the floating bed is divided into upper, middle, and lower layers. Each layer is equipped with corresponding sensors for real-time monitoring of parameters such as light intensity, temperature, and dissolved oxygen. These data provide a basis for the precise configuration of plants.

[0158] In the plant configuration stage, the system will select the first type of plants such as water hyacinth and Eichhornia crassipes that can efficiently absorb nitrogen and phosphorus according to the characteristics of sufficient light and suitable temperature in the upper layer. The middle layer is configured with the second type of plants such as reed and cattail that have strong heavy metal adsorption ability. The bottom layer has weak light, so the third type of plants such as Myriophyllum verticillatum and Hydrilla verticillata that are tolerant to weak light are selected.

[0159] To optimize the growth environment of plants, this application also sets up a rhizosphere environment monitoring system. This system uses micro-sensors to monitor parameters such as the pH value, redox potential, and organic matter content in the plant rhizosphere in real time. Based on these data, the system can precisely regulate the physical and chemical properties of the culture medium to create favorable conditions for the formation of the microbial community.

[0160] For example, when it is detected that the rhizosphere pH value deviates from the target range, the system will automatically add an appropriate amount of buffer for adjustment. Similarly, if the organic matter content is insufficient, the system will supplement organic nutrients. This refined regulation ensures that the plant roots and the microbial community can work together in the best state, thereby improving the water quality purification efficiency.

[0161] Through the above method, the present application achieves precise treatment of complex water body pollution. Compared with traditional single-layer floating beds, the multi-layer structure significantly improves the purification capacity per unit area. At the same time, by making full use of the environmental characteristics of different water layers, the problem of limited growth of plants in the middle and lower layers of traditional floating beds is solved. In addition, the precise regulation of the rhizosphere environment further optimizes the efficiency of the plant-microbe co-purification system.

[0162] As a preferred implementation mode, the present application can be applied to an urban lake affected by various pollutants, and the main pollutants include nitrogen, phosphorus and a small amount of heavy metals. Based on water quality monitoring data, the system selects to configure water hyacinths and water lettuce in the upper layer; reeds and cattails in the middle layer; and myriophyllum verticillatum and hydrilla verticillata in the bottom layer.

[0163] During the implementation process, the system monitors the water quality and plant growth conditions every 2 hours. When it detects that the decreasing rate of nitrogen and phosphorus concentrations in the upper layer decreases, the system will automatically adjust the coverage ratio of water hyacinths and water lettuce. At the same time, according to the change of heavy metal concentration in the middle layer, the planting density of reeds and cattails is adjusted. For the bottom layer, the system will adjust the planting depth of myriophyllum verticillatum and hydrilla verticillata in a timely manner according to the change of light intensity.

[0164] Compared with the prior art, the multi-layer ecological floating bed water quality purification method of the present application has the following advantages: First, through real-time monitoring and dynamic adjustment, it can better adapt to water quality changes and improve the purification efficiency. Second, the design of the multi-layer structure makes full use of the environmental characteristics of different depths of the water body, solving the problem of limited growth of plants in the middle and lower layers of traditional floating beds. Third, the precise regulation of the rhizosphere environment optimizes the plant-microbe co-purification system and further improves the purification efficiency. Finally, this method can not only effectively remove pollutants in the water body, but also improve the overall health of the aquatic ecosystem, achieving the dual goals of water quality purification and ecological restoration.

[0165] In some of the above embodiments, during the implementation of the present application, there is also a problem of how to precisely regulate the physicochemical properties of the culture medium to promote the formation of microbial communities.

[0166] In response to this, the present application further proposes a method for regulating the physicochemical properties of the culture medium according to rhizosphere environment parameters to promote the formation of microbial communities.

[0167] This method first obtains the rhizosphere environment parameters of the first type of plants, the second type of plants and the third type of plants, including pH value, redox potential and organic matter content. These parameters are key factors affecting the growth and activity of microorganisms. Then, the target parameter ranges required for the growth of microbial communities are obtained from a preset parameter database, including the target pH value range, the target redox potential range and the target organic matter content range. These target ranges are preset based on the optimal conditions for microbial growth.

[0168] Select a suitable microbial strain from a preset microbial strain database according to the target parameter range. This step ensures that the selected microbial strain can grow well under the target environmental conditions. Then, monitor the rhizosphere environmental parameters at preset detection time intervals. When the detected rhizosphere environmental parameters are not within the corresponding target range, select the corresponding regulator from a preset regulator database. The regulators include buffers for adjusting the pH value, potential regulators for adjusting the redox potential, and organic nutrients for supplementing organic matter.

[0169] Add the selected regulator to the culture medium according to the preset addition amount. Continue to monitor the rhizosphere environmental parameters at the preset detection time intervals until the rhizosphere environmental parameters reach and stabilize within the corresponding target range. Finally, inoculate the suitable microbial strain into the culture medium to promote the formation of the microbial community.

[0170] The method of this application creates optimal conditions for the formation of the microbial community by real-time monitoring and precise regulation of the rhizosphere environmental parameters. Specifically, the regulation of the pH value can be achieved by adding buffers, such as using phosphate buffer or bicarbonate solution. The regulation of the redox potential can be achieved by adding appropriate oxidants or reductants, such as hydrogen peroxide or ascorbic acid. The regulation of the organic matter content can be achieved by adding organic nutrients, such as humic acid or amino acid mixture.

[0171] Furthermore, the method of this application can customize the regulation of the rhizosphere environmental parameters according to the characteristics of different types of plants and the types of target pollutants. For example, for the first type of plants that absorb nitrogen and phosphorus, the pH value and organic matter content can be appropriately increased to promote the absorption of nitrogen and phosphorus; for the second type of plants that adsorb heavy metals, the pH value can be appropriately decreased and the redox potential can be increased to enhance the bioavailability of heavy metals.

[0172] Thus, the method of this application can not only precisely regulate the physical and chemical properties of the culture medium, but also customize the regulation according to the characteristics of different plants and pollutants, thereby providing optimal environmental conditions for the formation and growth of the microbial community. This refined regulation method helps to improve the water purification efficiency of the entire ecological floating bed system.

[0173] As a preferred implementation manner, the method of this application can be combined with an intelligent control system to achieve automatic regulation. Specifically, multiple sensors can be set to monitor the rhizosphere environmental parameters in real time and transmit the data to the central control unit. The control unit analyzes the data according to the preset algorithm, judges whether adjustment is needed and the specific parameters to be adjusted. Then, the control unit can automatically control the addition device of the regulator to accurately add the required regulator to the culture medium.

[0174] For example, assume that in a practical application scenario, the pH value of the initial rhizosphere environment is 6.2, the redox potential is 150 mV, and the organic matter content is 2%. The target parameter ranges are pH value 6.5 - 7.5, redox potential 200 - 300 mV, and organic matter content 3 - 5% respectively. The intelligent control system will first add an appropriate amount of sodium bicarbonate solution to increase the pH value, add a small amount of hydrogen peroxide to increase the redox potential, and add humic acid to increase the organic matter content. The system will continuously monitor the parameter changes until all parameters are stabilized within the target range.

[0175] This automated regulation method can not only improve the accuracy and efficiency of regulation, but also reduce the errors and costs of manual operations. At the same time, through continuous data collection and analysis, the system can also continuously optimize the regulation strategy to further improve the effect of microbial community formation.

[0176] The method of this application has significant advantages compared with the prior art. Traditional culture medium matrix regulation methods often adopt fixed formulas or rough adjustment methods, which are difficult to adapt to the characteristics of different plants and pollutants. The method proposed in this application can create the best growth environment for different types of plants and microorganisms through real-time monitoring and precise regulation. In addition, the method of this application also considers the dynamic process of microbial community formation, and ensures the stability and activity of the microbial community through continuous monitoring and regulation. This refined regulation method not only improves the water purification efficiency, but also enhances the adaptability and stability of the entire ecological floating bed system.

[0177] In some of the above embodiments, during the implementation of this application, there is also a problem of how to accurately calculate the water purification efficiency.

[0178] In response to this, this application further proposes that the method considers multiple influencing factors, including pollutant concentration, plant coverage area, water level change, etc., and comprehensively evaluates the purification efficiency of the system through a mathematical model.

[0179] Specifically, the water purification efficiency calculation method proposed in this application includes the following steps: taking the pollutant concentration in the water quality parameters as P(t), the plant coverage area in the plant growth status as C(t), and the water level change information as H(t), and substituting them into the following mathematical model to calculate the water purification efficiency:

[0180] E(t) = k1 * [1 - exp(-α * T(t))] * [P(t) / Pmax] * [1 - β * D(t)] * [C(t) / Cref] *

[0181] [1 + γ * sin(2π * t / 24)] * [1 + λ * (H(t) - Href) / Href] *

[0182] exp[-μ*(|ΔR(t)| / Rmax)]

[0183] where

[0184] E(t) represents the purification efficiency of the system at time t, and its value range is 0 - 1;

[0185] T(t) represents the system operation time, with the unit of hour;

[0186] P(t) represents the current pollutant concentration, with the unit of mg / L;

[0187] Pmax represents the maximum treatable pollutant concentration, with the unit of mg / L;

[0188] D(t) represents the water body disturbance degree, and its value range is 0 - 1;

[0189] C(t) represents the plant coverage, with the unit of m 2 ;

[0190] Cref represents the reference plant coverage, with the unit of m 2 ;

[0191] H(t) represents the current water level, with the unit of m;

[0192] Href represents the reference water level, with the unit of m;

[0193] ΔR(t) represents the rate of change of the flipping angle, with the unit of degree / hour;

[0194] Rmax represents the maximum allowable flipping angle, with the unit of degree;

[0195] k1 represents the basic purification efficiency coefficient, and its value range is 0.8 - 0.95;

[0196] α represents the time decay coefficient, and its value range is 0.01 - 0.05;

[0197] β represents the disturbance influence coefficient, and its value range is 0.1 - 0.3;

[0198] γ represents the light cycle influence coefficient, and its value range is 0.1 - 0.2;

[0199] λ represents the water level influence coefficient, and its value range is 0.2 - 0.4;

[0200] μ represents the flipping disturbance decay coefficient, and its value range is 0.1 - 0.3.

[0201] This mathematical model takes into account multiple influencing factors, including the system operation time T(t), pollutant concentration P(t), water body disturbance degree D(t), plant coverage C(t), water level H(t), and the rate of change of the flipping angle ΔR(t), etc. The model also includes multiple coefficients, such as the basic purification efficiency coefficient k1, the time decay coefficient α, the disturbance influence coefficient β, the light cycle influence coefficient γ, the water level influence coefficient λ, and the flipping disturbance decay coefficient μ, etc.

[0202] The value ranges of these coefficients can adapt to different water quality conditions and environmental factors. For example, the value range of the basic purification efficiency coefficient k1 is 0.8 - 0.95, which reflects the basic purification ability of the system. The value range of the time decay coefficient α is 0.01 - 0.05, indicating the degree of decay of the system efficiency over time. The value range of the disturbance influence coefficient β is 0.1 - 0.3, reflecting the influence of water body disturbance on the purification efficiency.

[0203] The model also introduces the periodic function sin(2π*t / 24) to simulate the influence of the light cycle on the purification efficiency. The value range of the γ coefficient is 0.1 - 0.2, which can be adjusted according to the actual light conditions. The value range of the water level influence coefficient λ is 0.2 - 0.4, used to adjust the influence degree of water level change on the purification efficiency. The value range of the flipping disturbance decay coefficient μ is 0.1 - 0.3, reflecting the disturbance influence of the floating bed flipping on the water body.

[0204] Through this mathematical model, this application can calculate the water quality purification efficiency more accurately. This model not only considers basic factors such as pollutant concentration and plant coverage, but also introduces dynamic factors such as water level change, light cycle, and floating bed flipping, thus more comprehensively reflecting the complex situation in the actual water quality purification process.

[0205] For example, in a specific embodiment, assume the parameters at a certain moment t are as follows:

[0206] Pollutant concentration P(t) = 5mg / L;

[0207] The maximum treatable pollutant concentration Pmax = 10mg / L;

[0208] Water body disturbance degree D(t) = 0.2;

[0209] Plant coverage C(t) = 80m 2 ;

[0210] Reference plant coverage Cref = 100m 2 ;

[0211] Current water level H(t) = 1.2m;

[0212] Reference water level Href = 1.0m;

[0213] The flip angle change rate ΔR(t) = 5 degrees per hour;

[0214] The maximum allowable flip angle Rmax = 30 degrees;

[0215] The system operation time T(t) = 100 hours;

[0216] Select coefficients:

[0217] k1 = 0.9, α = 0.03, β = 0.2, γ = 0.15, λ = 0.3, μ = 0.2;

[0218] Substitute these parameters into the model for calculation:

[0219] E(t) = 0.9 * [1 - exp(-0.03 * 100)] * [5 / 10] * [1 - 0.2 * 0.2] * [80 / 100] *

[0220] [1 + 0.15 * sin(2π * 100 / 24)] * [1 + 0.3 * (1.2 - 1.0) / 1.0] *

[0221] exp[-0.2 * (|5| / 30)]

[0222] The value of E(t) obtained through calculation is the water quality purification efficiency at that moment. This efficiency value comprehensively considers multiple influencing factors and can more accurately reflect the actual purification situation.

[0223] The water quality purification efficiency calculation method proposed in this application has the following advantages:

[0224] First, this method considers multiple influencing factors, including pollutant concentration, plant coverage, water level change, light cycle, and floating bed flipping, etc., and can more comprehensively reflect the complex situation in the actual water quality purification process.

[0225] Second, by introducing multiple adjustable coefficients, such as the basic purification efficiency coefficient, time decay coefficient, etc., this method has strong adaptability and flexibility and can be adjusted according to different water quality conditions and environmental factors.

[0226] Third, this method introduces a periodic function to simulate the influence of the light cycle. This innovative design enables the calculation result to better reflect the influence of day and night changes on the purification efficiency.

[0227] Finally, this method considers the disturbance effect of floating bed flipping on the water body. By introducing the flip angle change rate and the flip disturbance attenuation coefficient, it can more accurately evaluate the influence of floating bed flipping on the purification efficiency.

[0228] Compared with the prior art, the water quality purification efficiency calculation method proposed in this application has obvious advantages. Traditional calculation methods usually only consider the change of pollutant concentration and ignore the influence of other important factors. The method of this application greatly improves the calculation accuracy and adaptability by introducing multiple influencing factors and adjustable coefficients. In addition, the method of this application also considers dynamic factors such as light cycle and floating bed flipping, which are generally lacking in the prior art. Through this comprehensive and accurate calculation method, the operation and optimization of the ecological floating bed system can be better guided, thereby improving the effect of water quality purification.

[0229] In a second aspect, with reference to Figure 2 , this application further proposes a water pollution prevention and control system based on an ecological floating bed. The system includes:

[0230] A data acquisition module 210 for obtaining water quality parameters, plant growth conditions, and water level change information;

[0231] An efficiency calculation module 220 for calculating the water quality purification efficiency according to the water quality parameters, plant growth conditions, and water level change information;

[0232] A parameter determination module 230 for determining the floating bed flipping frequency, flipping speed, and floating bed height according to the difference between the water quality purification efficiency and the preset target efficiency;

[0233] An execution control module 240 for controlling the floating bed to perform the flipping action of the flipping frequency and flipping speed and the adjustment action of the floating bed height to improve the water quality purification efficiency and minimize the interference to the aquatic ecosystem. The water pollution prevention and control system proposed in this application realizes the intelligent control of the ecological floating bed through the coordinated work of multiple functional modules.

[0234] By obtaining water quality parameters, plant growth conditions, and water level change information, calculating the water quality purification efficiency, and dynamically adjusting the floating bed flipping frequency, flipping speed, and height according to the difference between the purification efficiency and the target efficiency, the real-time response to water quality changes, the optimization of the multi-layer floating bed structure, the intelligent control of the floating bed flipping, the automatic adaptation to water level changes, and the balance between purification efficiency and ecological protection are realized. It has the advantages of real-time response to water quality changes, optimization of the multi-layer floating bed structure, intelligent control of the floating bed flipping, automatic adaptation to water level changes, and balance between purification efficiency and ecological protection.

[0235] In addition, in some preferred embodiments, a water pollution prevention and control system based on an ecological floating bed proposed in this application can execute any one of the steps in the above method.

[0236] The above are only embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, various modifications and changes can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A water pollution prevention and control method based on an ecological floating bed, characterized in that, The steps of the method include: Obtain water quality parameters, plant growth conditions, and water level change information; Calculate the water quality purification efficiency based on the water quality parameters, plant growth conditions, and water level change information; Determine the floating bed flipping frequency, flipping speed, and floating bed height according to the difference between the water quality purification efficiency and the preset target efficiency; Control the floating bed to perform the flipping action with the flipping frequency and flipping speed and the adjustment action of the floating bed height to improve the water quality purification efficiency and minimize the interference to the aquatic ecosystem; The step of controlling the floating bed to perform the flipping action with the flipping frequency and flipping speed and the adjustment action of the floating bed height to improve the water quality purification efficiency and minimize the interference to the aquatic ecosystem includes: Obtain water quality parameters within a preset monitoring period; Calculate the change rate of the water quality purification efficiency based on the water quality parameters to obtain the water quality change trend; When the water quality change trend shows that the water quality purification efficiency continuously decreases, start the aquatic biological activity monitoring device to obtain aquatic biological activity intensity data; Determine the execution time period with the lowest aquatic biological activity intensity within a preset time range according to the aquatic biological activity intensity data; Within the execution time period: Obtain the preset control parameter adjustment step size; Adjust the flipping frequency, flipping speed, and floating bed height in sequence according to the step size; Obtain the adjusted water quality purification efficiency; When the water quality purification efficiency reaches the preset target efficiency, keep the current control parameters unchanged.

2. The water pollution prevention and control method based on an ecological floating bed according to claim 1, characterized in that, The step of when the water quality change trend shows that the water quality purification efficiency continuously decreases, start the aquatic biological activity monitoring device to obtain aquatic biological activity intensity data includes: Obtain the historical data of the water quality purification efficiency within a preset monitoring period; Obtain the preset change rate threshold of the water quality purification efficiency; Based on the historical data, calculate the ratio of the difference in water quality purification efficiency between adjacent time points to the time interval to obtain the water quality purification efficiency change rate; When the change rate is negative in three consecutive monitoring periods and its absolute value exceeds the change rate threshold, determine that the water quality purification efficiency continuously decreases; Determine the sampling frequency of the aquatic biological activity monitoring device according to the corresponding relationship between the absolute value of the change rate and the preset sampling frequency, where the larger the absolute value of the change rate, the higher the sampling frequency; Collect aquatic biological activity intensity data according to the sampling frequency.

3. A water pollution prevention and control method based on an ecological floating bed according to claim 1, characterized in that, The step of determining the execution time period with the lowest aquatic biological activity intensity within a preset time range according to the aquatic biological activity intensity data includes: Obtain the preset monitoring time range and the time window division parameter N, where N is an integer greater than 1; Within the monitoring time range, obtain aquatic biological activity intensity data at multiple time points according to the preset sampling interval; Divide the monitoring time range into N consecutive time windows according to the time window division parameter N; Calculate the arithmetic mean of all aquatic biological activity intensity data within each time window; Compare the averages of the N time windows, and determine the time window with the lowest average as the execution time period.

4. The water pollution prevention and control method based on an ecological floating bed according to claim 1, characterized in that, The steps of determining the floating bed flipping frequency, flipping speed, and floating bed height according to the difference between the water quality purification efficiency and the preset target efficiency include: Obtain the preset water quality parameter monitoring period; Obtain the water quality purification efficiency adjustment coefficient and the control parameter adjustment step size within the monitoring period; Obtain the preset priority order of control parameters, where the floating bed flipping frequency has the highest priority, followed by the flipping speed, and the floating bed height has the lowest priority; Calculate the difference between the water quality purification efficiency and the preset target efficiency; Multiply the difference by the water quality purification efficiency adjustment coefficient to obtain the total control parameter adjustment amount; Divide the total control parameter adjustment amount by the control parameter adjustment step size to obtain the number of adjustments; According to the priority order, within their respective preset adjustment ranges, sequentially adjust the floating bed flipping frequency, flipping speed, and floating bed height, where: When adjusting the floating bed flipping frequency, the adjustment amplitude each time is the control parameter adjustment step size and does not exceed the preset maximum flipping frequency; When adjusting the flipping speed, the adjustment amplitude each time is the control parameter adjustment step size and does not exceed the preset maximum flipping speed; When adjusting the floating bed height, the adjustment amplitude each time is the control parameter adjustment step size and does not exceed the preset maximum adjustment height.

5. The water pollution prevention and control method based on an ecological floating bed according to claim 4, characterized in that, The steps of obtaining the water quality purification efficiency adjustment coefficient and the control parameter adjustment step size within the monitoring period include: Obtain the preset historical data collection parameters, including the number of historical monitoring periods N and the reference adjustment coefficient; Continuously obtain the water quality purification efficiency data for N historical monitoring periods, where N is an integer greater than or equal to 10; Calculate the sum of squared deviations of the water quality purification efficiency data within the N historical monitoring periods and divide it by N - 1 to obtain the variance; Perform a square root operation on the variance to obtain the standard deviation; Multiply the standard deviation by the reference adjustment coefficient to obtain the water quality purification efficiency adjustment coefficient; Obtain the preset minimum number of adjustments and maximum number of adjustments; Within the N historical monitoring periods, calculate the absolute value of the difference in water quality purification efficiency between adjacent two monitoring periods, and select the maximum value as the maximum change amount; Divide the maximum change amount by the maximum number of adjustments to obtain the control parameter adjustment step size.

6. The water pollution prevention and control method based on an ecological floating bed according to claim 1, characterized in that, The method further includes: Obtain the concentration data and type information of various pollutants in the water body from a preset water quality monitoring system; According to the concentration data and type information of the pollutants, obtain the corresponding plant configuration plan from a preset plant database; Divide the floating bed into upper, middle, and lower layers, and obtain the light intensity, temperature, and dissolved oxygen data of each layer; According to the plant configuration plan and the light intensity, temperature, and dissolved oxygen data of the upper layer, configure the first type of plants for absorbing nitrogen and phosphorus in the upper layer; According to the plant configuration plan and the light intensity, temperature, and dissolved oxygen data of the middle layer, configure the second type of plants for adsorbing heavy metals in the middle layer; According to the plant configuration plan and the light intensity, temperature, and dissolved oxygen data of the lower layer, configure the third type of plants tolerant to weak light in the lower layer; Monitor the rhizosphere environment parameters of the first type of plants, the second type of plants, and the third type of plants; Regulate the physical and chemical properties of the culture medium according to the rhizosphere environmental parameters to promote the formation of the microbial community; Through the synergistic effects of the root exudates of the first type of plant, the second type of plant, and the third type of plant and the microbial community, the purification of the pollutants is achieved.

7. A water pollution prevention and control method based on an ecological floating bed according to claim 6, characterized in that, The steps of regulating the physical and chemical properties of the culture medium according to the rhizosphere environmental parameters to promote the formation of the microbial community include: Obtain the rhizosphere environmental parameters of the first type of plant, the second type of plant, and the third type of plant, where the rhizosphere environmental parameters include pH value, redox potential, and organic matter content; Obtain the target parameter ranges required for the growth of the microbial community from a preset parameter database, including the target pH value range, the target redox potential range, and the target organic matter content range; Select the suitable microbial strains according to the target parameter ranges from a preset microbial strain database; Monitor the rhizosphere environmental parameters at preset detection time intervals; When the detected rhizosphere environmental parameters are not within the corresponding target ranges, select the corresponding regulators from a preset regulator database, where the regulators include: Buffers for adjusting the pH value, potential regulators for adjusting the redox potential, and organic nutrients for supplementing organic matter; Add the selected regulators to the culture medium according to the preset addition amounts; Continue to monitor the rhizosphere environmental parameters at the preset detection time intervals until the rhizosphere environmental parameters reach and stabilize within the corresponding target ranges; Inoculate the suitable microbial strains into the culture medium to promote the formation of the microbial community.

8. A water pollution prevention and control method based on an ecological floating bed according to claim 1, characterized in that, The steps of calculating the water quality purification efficiency according to the water quality parameters, plant growth conditions, and water level change information specifically include: Take the pollutant concentration in the water quality parameters as P(t), the plant coverage area in the plant growth conditions as C(t), and the water level change information as H(t), and substitute them into the following mathematical model to calculate the water quality purification efficiency: E(t) = k1 * [1 - exp(-α * T(t))] * [P(t) / Pmax] * [1 - β * D(t)] * [C(t) / Cref] * [1 + γ * sin(2π * t / 24)] * [1 + λ * (H(t) - Href) / Href] * exp[-μ * (|ΔR(t)| / Rmax)] Where, E(t) represents the purification efficiency of the system at time t, and the value range is 0 - 1; T(t) represents the system operation time, with the unit of hour; P(t) represents the current pollutant concentration, with the unit of mg / L; Pmax represents the maximum treatable pollutant concentration, with the unit of mg / L; D(t) represents the water body disturbance degree, and the value range is 0 - 1; C(t) represents the plant coverage, with the unit of m 2 ; Cref represents the reference plant coverage, with the unit of m 2 ; H(t) represents the current water level, with the unit of m; Href represents the reference water level, with the unit of m; ΔR(t) represents the flip angle change rate, with the unit of degree / hour; Rmax represents the maximum allowable flip angle, with the unit of degree; k1 represents the basic purification efficiency coefficient, and the value range is 0.8 - 0.95; α represents the time decay coefficient, and the value range is 0.01 - 0.05; β represents the disturbance influence coefficient, and its value range is 0.1 - 0.3; γ represents the light cycle influence coefficient, and its value range is 0.1 - 0.2; λ represents the water level influence coefficient, and its value range is 0.2 - 0.4; μ represents the flipping disturbance attenuation coefficient, and its value range is 0.1 - 0.

3.

9. A water pollution prevention and control system based on an ecological floating bed, characterized in that, The system includes: A data acquisition module for obtaining water quality parameters, plant growth conditions, and water level change information; An efficiency calculation module for calculating the water quality purification efficiency according to the water quality parameters, plant growth conditions, and water level change information; A parameter determination module for determining the floating bed flipping frequency, flipping speed, and floating bed height according to the difference between the water quality purification efficiency and the preset target efficiency; An execution control module for controlling the floating bed to perform the flipping action of the flipping frequency and flipping speed and the adjustment action of the floating bed height to improve the water quality purification efficiency and minimize the interference to the aquatic ecosystem; The step of controlling the floating bed to perform the flipping action of the flipping frequency and flipping speed and the adjustment action of the floating bed height to improve the water quality purification efficiency and minimize the interference to the aquatic ecosystem includes: Obtaining water quality parameters within a preset monitoring period; Calculating the change rate of the water quality purification efficiency based on the water quality parameters to obtain the water quality change trend; When the water quality change trend shows that the water quality purification efficiency continuously decreases, starting an aquatic biological activity monitoring device to obtain aquatic biological activity intensity data; Determining the execution time period with the lowest aquatic biological activity intensity within a preset time range according to the aquatic biological activity intensity data; Within the execution time period: Obtaining a preset control parameter adjustment step size; Sequentially adjusting the flipping frequency, flipping speed, and floating bed height according to the step size; Obtaining the adjusted water quality purification efficiency; When the water quality purification efficiency reaches the preset target efficiency, keeping the current control parameters unchanged.

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