Water pollution control method and system based on ecological floating bed

Abstract

The invention provides a water pollution prevention and control method and system based on an ecological floating bed, and relates to the technical field of water pollution prevention and control. Calculating water quality purification efficiency according to the water quality parameters, the plant growth condition and the water level change information; according to a difference value between the water quality purification efficiency and preset target efficiency, determining a floating bed overturning frequency, an overturning speed and a floating bed height; and controlling the floating bed to execute the overturning action of the overturning frequency and the overturning speed and the adjusting action of the height of the floating bed, so as to improve the water quality purification efficiency and minimize the interference on the aquatic ecosystem. The water pollution prevention and control method and system based on the ecological floating bed have the advantages that the method and system can adapt to dynamically changing water quality and seasonal pollutant concentration fluctuation, and the influence on an aquatic ecosystem is minimized.

Description

Technical Field

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

[0002] In a river that is heavily polluted by industry, an ecological floating bed can be used for water pollution prevention and control. The river flows through several industrial areas, and the water quality and types of pollutants vary with the seasons and factory production cycles. In order to adapt to this change, an adjustable multi-layer ecological floating bed system was designed. However, as the floating bed system was used for a longer time, the growth of the bottom plants was affected by the shading of the upper plants, resulting in a decrease in purification efficiency. To solve this problem, the floating bed was designed to flip regularly so that all plants can get enough sunlight. However, this flipping mechanism temporarily disturbs the water body during operation, affecting the habitat of aquatic organisms. At the same time, due to the seasonal changes in the river water level, the floating bed system also needs to be able to automatically adjust its height as the water level rises and falls.

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

[0004] The system lacks the ability to respond to changes in water quality in real time and cannot adjust purification strategies in time 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] Although the multi-layer floating bed structure increases the total amount of plants per unit area, the lack of light in the lower layer seriously affects the purification efficiency. Long-term lack of light will cause the lower layer plants to grow slowly or even wither, greatly reducing the purification capacity of the entire system.

[0006] Although the floating bed flipping mechanism can solve the problem of uneven light, frequent flipping will disturb the water body and affect the stability of the aquatic ecosystem. Excessive disturbance may cause an increase in suspended matter in the water body, affecting water quality, and also disrupting the normal life of aquatic animals and destroying the ecological balance.

[0007] The existing system lacks the ability to automatically adapt to water level changes, making it difficult to ensure the best contact between plant roots and water. Water level changes will affect the contact area between plant roots and water, thereby affecting purification efficiency. If the height of the floating bed cannot be adjusted in time, the plant roots may be exposed to the air or completely submerged in water, which will reduce the purification effect.

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

[0009] In addition, existing systems usually use fixed operating parameters, such as fixed turnover frequency and speed. This rigid control method cannot adapt to the complex and changing water environment. The water quality and biological activity patterns vary in different seasons and time periods. Fixed operating parameters are difficult to maintain the best effect in all situations.

[0010] At the same time, the existing system lacks a real-time monitoring and feedback mechanism for plant growth conditions. The growth conditions of plants directly affect the purification efficiency, but the existing system often ignores this point and fails to adjust the operation strategy according to the actual growth conditions of plants.

[0011] Finally, existing systems are not effective in treating complex, multi-pollutant situations. Different types of pollutants may require different treatment methods and plant species, but existing systems often use a single plant configuration that is difficult to handle complex pollution situations.

[0012] In view of the above problems, the existing technology needs to be improved urgently. 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 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 of 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 the method include: obtaining water quality parameters, plant growth conditions and water level change information; calculating the water 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 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, so as to improve the water purification efficiency and minimize the interference with the aquatic ecosystem.

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

[0017] Furthermore, the present application also proposes that when the water quality change trend shows that the water purification efficiency is continuously decreasing, the aquatic biological activity monitoring device is started, and the step of obtaining the aquatic biological activity intensity data includes: obtaining the historical data of the water purification efficiency within a preset monitoring period; obtaining a preset threshold value of the change rate of the water purification efficiency; based on the historical data, calculating the ratio of the difference in water purification efficiency between adjacent time points to the time interval to obtain the water purification efficiency change rate; when the change rate is negative for three consecutive monitoring periods and its absolute value exceeds the change rate threshold, it is determined that the water purification efficiency is continuously decreasing; according to the correspondence 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, wherein the larger the absolute value of the change rate, the higher the sampling frequency; and the aquatic biological activity intensity data is collected according to the sampling frequency.

[0018] Furthermore, the present application also proposes that the step of determining the execution time period with the lowest aquatic biological activity intensity within a preset time range based on the aquatic biological activity intensity data includes: 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; dividing the monitoring time range into N continuous time windows according to the time window division parameter N; calculating the arithmetic mean of all aquatic biological activity intensity data in each time window; comparing the average values ​​of the N time windows, and determining the time window with the lowest average value 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 within the monitoring period; obtaining a preset control parameter priority order, wherein 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 a total control parameter adjustment step. The method comprises the following steps: dividing the total control parameter adjustment amount by the control parameter adjustment step 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, wherein: when adjusting the floating bed flipping frequency, the amplitude of each adjustment is the control parameter adjustment step and does not exceed the preset maximum flipping frequency; when adjusting the flipping speed, the amplitude of each adjustment is the control parameter adjustment step and does not exceed the preset maximum flipping speed; when adjusting the floating bed height, the amplitude of each adjustment is the control parameter adjustment step 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 within the monitoring period includes: obtaining preset historical data collection parameters, including the number of historical monitoring periods N and the benchmark adjustment coefficient; continuously obtaining water purification efficiency data of N historical monitoring periods, where N is an integer greater than or equal to 10; calculating the sum of squares of 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 benchmark 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 two adjacent monitoring periods, and selecting the maximum value as the maximum change; dividing the maximum change by the maximum number of adjustments to obtain the control parameter adjustment step.

[0021] Furthermore, the present application also proposes that the method also includes: obtaining concentration data and type information of various pollutants in the water body from a preset water quality monitoring system; obtaining corresponding plant configuration schemes 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 light intensity, temperature and dissolved oxygen data of each layer; configuring a first type of plant for absorbing nitrogen and phosphorus in the upper layer according to the plant configuration scheme and the light intensity, temperature and dissolved oxygen data of the upper layer; configuring a second type of plant for adsorbing heavy metals in the middle layer according to the plant configuration scheme and the light intensity, temperature and dissolved oxygen data of the middle layer; configuring a third type of plant that is resistant to weak light in the bottom layer according to the plant configuration scheme and the light intensity, temperature and dissolved oxygen data of the bottom layer; monitoring the rhizosphere environmental parameters of the first type of plant, the second type of plant and the third type of plant; regulating the physical and chemical properties of the culture matrix according to the rhizosphere environmental parameters to promote the formation of a microbial community; and purifying the pollutants through the root secretions of the first type of plant, the second type of plant and the third type of plant and the synergistic effect of the microbial community.

[0022] Furthermore, the present application also proposes that the step of regulating the physicochemical properties of the culture matrix according to the rhizosphere environmental parameters to promote the formation of the microbial community includes: obtaining the rhizosphere environmental parameters of the first type of plants, the second type of plants and the third type of plants, the rhizosphere environmental parameters including pH value, redox potential and organic matter content; obtaining the target parameter range required for the growth of the microbial community from a preset parameter database, including a target pH value range, a target redox potential range and a target organic matter content range; selecting an adapted microbial strain from a preset microbial strain database according to the target parameter range; and selecting a suitable microbial strain according to the preset detection method. The method comprises the steps of: monitoring the rhizosphere environmental parameters at a detection time interval; when the detected rhizosphere environmental parameters are not within the corresponding target range, selecting a corresponding regulator from a preset regulator database, wherein the regulator includes: a buffer for adjusting the pH value, a potential regulator for adjusting the redox potential, and an organic nutrient for supplementing organic matter; adding the selected regulator to the culture matrix according to a preset addition amount; continuing to monitor the rhizosphere environmental parameters at the preset detection time interval until the rhizosphere environmental parameters reach and stabilize within the corresponding target range; inoculating the adapted microbial strain into the culture matrix to promote the formation of a microbial community.

[0023] Furthermore, the present application also proposes that the step of calculating the water purification efficiency based on 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 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, with a value range of 0-1; T(t) represents the system operation time, in hours; P(t) represents the current pollutant concentration, in mg / L; Pmax represents the maximum treatable pollutant concentration, in mg / L; D(t) represents the water disturbance, with a value range of 0-1; C(t) represents the plant coverage, in m 2 ; Cref represents the reference plant coverage, in m 2 ; H(t) represents the current water level in m; Href represents the reference water level in m; ΔR(t) represents the rate of change of the flip angle in degrees / hour; Rmax represents the maximum allowable flip angle in degrees; k1 represents the basic purification efficiency coefficient, ranging from 0.8 to 0.95; α represents the time attenuation coefficient, ranging from 0.01 to 0.05; β represents the disturbance influence coefficient, ranging from 0.1 to 0.3; γ represents the light cycle influence coefficient, ranging from 0.1 to 0.2; λ represents the water level influence coefficient, ranging from 0.2 to 0.4; μ represents the flip disturbance attenuation coefficient, ranging from 0.1 to 0.3.

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

[0025] From the above, it can be seen that the intelligent water pollution prevention and control system and method based on the ecological floating bed provided by the present application calculates the water 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, thereby realizing real-time response to water quality changes, optimization of the multi-layer floating bed structure, intelligent control of the flipping of the floating bed, 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 the flipping of the floating bed, automatic adaptation to water level changes, and balance of purification efficiency and ecological protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart of an intelligent water pollution prevention and control method based on an ecological floating bed is provided for this application.

[0027] Figure 2 A schematic diagram of an intelligent water pollution prevention and control system based on an ecological floating bed provided for this application.

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

[0029] The technical solutions in the present application will be clearly and completely described below 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 generally 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 for protection, but merely 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 making creative work 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, so 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 this application, the terms "first", "second", etc. are only used to distinguish the description 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 capabilities to water quality changes, and the inability to adjust the purification strategy in time according to the dynamic changes in pollutant concentrations. Secondly, although the multi-layer structure increases the total amount of plants per unit area, the lack of light in the lower layer 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 between plant roots and water bodies. Most importantly, the existing system lacks an intelligent control strategy that comprehensively considers water quality, plant growth and ecological impacts, and it is difficult to strike a balance between purification efficiency and ecological protection. These problems directly affect the water purification efficiency, plant growth conditions and the health of aquatic ecosystems, and restrict the application effect of ecological floating bed technology in complex water environments.

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

[0033] If these technical problems cannot be effectively solved, a series of serious consequences will occur. First, the low efficiency of water purification will cause the accumulation rate of pollutants to exceed the treatment rate, resulting in a continuous deterioration of water quality. Secondly, the uneven growth of plants will cause some plants to die prematurely, increase the system maintenance cost and replacement frequency. Furthermore, the continuous interference with aquatic ecosystems will reduce biodiversity, destroy the self-purification capacity of water bodies, and form a vicious circle. In the long run, this will not only affect the effect of water quality improvement, but also reduce the stability and recovery capacity of the entire ecosystem. In addition, due to the low efficiency of the system, it may be necessary to increase the floating bed area to achieve the expected purification effect, which will occupy more water surface space and affect other functions of the river. Therefore, there is an urgent need for an ecological floating bed system that can be intelligently regulated to adapt to the complex and changeable water environment, while improving the efficiency of water purification and protecting the aquatic ecosystem to the maximum extent.

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

[0035] Next, we considered a solution of independent multi-parameter adjustment, that is, adjusting different control parameters according to water quality, plant growth and water level changes. However, this method may lead to conflicts among various adjustments, making it difficult to achieve the best balance.

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

[0037] Next, this application innovatively proposes a method for determining control parameters based on the difference between the water purification efficiency and the preset target efficiency. The control parameters include the floating bed flipping frequency, flipping speed and floating bed height. This method can dynamically adjust the system operating parameters according to the actual purification effect, and realize adaptive control of complex environments.

[0038] Finally, the present application designs an execution control step, which improves the water purification efficiency while minimizing the interference to the aquatic ecosystem by precisely controlling the flipping action and height adjustment of the floating bed.

[0039] Therefore, refer to Figure 1 This application proposes a water pollution prevention and control method based on an ecological floating bed, the steps of the method include:

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

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

[0042] S130, 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;

[0043] S140, 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 purification efficiency and minimize the interference to the aquatic ecosystem.

[0044] Among them, water quality parameters refer to various indicators that reflect the degree of water pollution, which can be achieved by including chemical oxygen demand (COD), biochemical oxygen demand (BOD), total nitrogen, total phosphorus, dissolved oxygen and other indicators.

[0045] Among them, plant growth conditions refer to various indicators that reflect the health of plants on the floating bed, which can be specifically achieved through indicators such as chlorophyll content, plant height, root development, and biomass.

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

[0047] Among them, water purification efficiency refers to the degree to which pollutants are removed per unit time, which can be calculated using a preset mathematical model.

[0048] The floating bed flipping frequency refers to the number of times the floating bed completes flipping per unit time, which can be specifically achieved by the number of flippings per hour.

[0049] The turning speed refers to the time required for the floating bed to complete one turning, which can be specifically achieved by an angular velocity of degrees per second.

[0050] The floating bed height refers to the vertical distance between the bottom of the floating bed and the water surface, which can be achieved by using an adjustable supporting structure.

[0051] The core innovation of this application is to propose a method that calculates the water purification efficiency by obtaining water quality parameters, plant growth conditions and water level change information, and dynamically adjusts the floating bed's flipping frequency, flipping speed and height 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 aquatic ecosystems while improving water purification efficiency.

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

[0053] Next, based on the acquired water quality parameters, plant growth conditions and water level change information, a preset mathematical model is used to calculate the current water purification efficiency. The model takes into account the impact of factors such as pollutant concentration, plant coverage area, and water level on purification efficiency.

[0054] The calculated water purification efficiency is then compared with the preset target efficiency to obtain the difference. Based on this difference, the system uses an optimization algorithm to determine the floating bed flip frequency, flip speed, and floating bed height that need to be adjusted. The key to this step is to find a combination of parameters that can maximize the purification efficiency while minimizing the disturbance to the aquatic ecosystem.

[0055] Finally, according to the determined parameters, instructions are sent to the mechanical control system of the floating bed to achieve the flipping and height adjustment of the floating bed. The flipping action ensures that the plants on each layer can receive light evenly, and the height adjustment ensures that the plant roots maintain optimal contact with the water.

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

[0057] As a preferred embodiment, the present application can be implemented in a river section polluted by industry. For example, the river section is 500 meters long, 30 meters wide, and has an average water depth of 3 meters. The floating bed system consists of 10 independent rectangular floating bed units, each unit is 10 meters × 5 meters in size, with a total coverage area of ​​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 30 cm high and 20 cm between layers. The upper layer is planted with floating plants such as water hyacinth and water hyacinth, the middle layer is planted with emergent plants such as reeds and cattails, and the lower layer is planted with submerged plants such as foxtail algae and black algae. The floating bed frame is made of high-density polyethylene material, which has good corrosion resistance and durability.

[0059] The online water quality monitoring equipment collects water samples every 15 minutes and analyzes indicators such as COD, total nitrogen, and total phosphorus. The plant growth status sensor includes a chlorophyll fluorescence meter and an image analysis system, which scans the plant growth status once an hour. The water level monitoring device uses an ultrasonic water level meter with an accuracy of ±1 cm, and records water level data every 5 minutes.

[0060] The flipping mechanism of the floating bed adopts an electric gear transmission system, which can achieve a flipping angle of 0-180 degrees, and the flipping speed can be adjusted between 0.1-1 degrees / 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. The software is based on machine learning algorithms and can predict the optimal control strategy based on historical data and current environmental parameters. The system performs parameter optimization calculations every 30 minutes and adjusts the floating bed operating parameters based on the calculation results.

[0062] In some embodiments, when a sudden rise of 20% in COD concentration is detected, the system will increase the frequency of floating bed turnover from 2 times a day to 3 times a day, and reduce the turnover speed from 0.5 degrees / second to 0.3 degrees / second to increase the contact time between plants and water. When the water level rises by 30 centimeters, the system will raise the floating bed by 20 centimeters accordingly to ensure that the plant roots are always in the best absorption position.

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

[0064] In this regard, the present application further proposes that the method adopts a series of refined control strategies when 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.

[0065] The method first obtains water quality parameters within a preset monitoring period, and calculates the change rate of 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 continues to decrease, 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, based on the acquired data on the intensity of aquatic biological activity, determine the execution time period with the lowest intensity of aquatic biological activity within the preset time range. This step is designed to take into account the activity patterns of aquatic organisms. Choosing the time period when biological activity is least active to adjust the floating bed can minimize interference with the ecosystem.

[0067] In the determined execution time period, this method adopts a progressive adjustment strategy. First, the preset control parameter adjustment step is obtained, and then the flipping frequency, flipping speed and floating bed height are adjusted in sequence according to this step. 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 severe disturbance of the water body caused by excessive parameter adjustment.

[0068] A key feature of this method is the introduction of a device for monitoring the activity of aquatic organisms. This device can be an underwater acoustic sensor, an optical sensor, or a bioelectric signal detector. Through these sensors, the activity of aquatic organisms can be monitored in real time, such as the swimming frequency of fish, the density changes of plankton, etc. 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 adjusted dynamically according to the severity of the water quality change. For example, when the water quality deteriorates rapidly, the step size can be appropriately increased to speed up the adjustment speed; when the water quality changes slowly, the step size can be reduced to achieve more precise control.

[0070] In practical applications, this method can be used in conjunction with an intelligent control system. For example, a prediction model based on a machine learning algorithm can be set up to predict the water quality trend and aquatic biological activity patterns in the future based on historical data. This allows the adjustment strategy of the floating bed to be planned in advance and the execution time to be further optimized.

[0071] Specifically, when it is detected that the water purification efficiency has been continuously reduced, the system will first analyze historical data to determine the daily cycle pattern of aquatic biological activity. Suppose it is found that the intensity of aquatic biological activity 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 gradually adjust the parameters according to the preset step amount, such as adjusting the flip frequency by 0.1 times / hour, the flip speed by 0.5 degrees / second, and the floating bed height by 1 cm each time. After each adjustment, the system will wait for a preset stabilization time, such as 30 minutes, and then re-test the water purification efficiency. If the efficiency has increased but has not yet reached the target, the system will continue to adjust; if the efficiency has reached the target, the system will stop adjusting and record the current parameters.

[0072] Through this refined control strategy, this method can improve water purification efficiency while minimizing interference with aquatic ecosystems. Compared with traditional fixed parameter control, this method can better adapt to complex and changing water environments and achieve a balance between water purification and ecological protection.

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

[0074] In general, the method proposed in this application effectively solves the problem of how to accurately control the flipping and height adjustment of the floating bed to minimize the interference with the aquatic ecosystem when the water purification efficiency decreases by introducing innovative points such as monitoring of aquatic biological activities, selecting the best execution time period, and adopting a progressive adjustment strategy. This method not only improves the efficiency of water 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-mentioned embodiments, during the implementation of the present application, there is still the problem of how to accurately judge the continuous decline in water purification efficiency and promptly start monitoring of aquatic biological activities.

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

[0077] The technical solution of the present application obtains the historical data of water purification efficiency within a preset monitoring period, sets a threshold value for the change rate of water purification efficiency, calculates the ratio of the difference in water purification efficiency between adjacent time points and the time interval, and obtains the change rate of water purification efficiency. When the change rate is negative for three consecutive monitoring periods and its absolute value exceeds the change rate threshold, it is determined that the water purification efficiency has been continuously decreasing. Subsequently, the sampling frequency of the aquatic biological activity monitoring device is determined based on the correspondence between the absolute value of the change rate and the preset sampling frequency, wherein the greater 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] The technical solution of the present application introduces the concept of water purification efficiency change rate and sets a change rate threshold, which can more accurately determine whether the water purification efficiency has continuously decreased. By making judgments over three consecutive monitoring cycles, misjudgments caused by short-term fluctuations can be effectively avoided, thereby improving the accuracy and reliability of the judgment.

[0079] In addition, the technical solution of this 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 can be used to capture the changes in the activities of aquatic organisms more timely; when the decline rate is slow, a lower sampling frequency can be used to reduce unnecessary energy consumption and data processing burden.

[0080] When implementing it, you can take the following steps:

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

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

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

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

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

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

[0087] It can be seen that the change rate in three consecutive monitoring cycles is negative, 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 has been continuously declining.

[0088] The sampling frequency is set according to the correspondence between the absolute value of the rate of change and the preset sampling frequency. For example, the following correspondence can be formulated:

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

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

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

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

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

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

[0095] Compared with the prior art, the technical solution of this application introduces the concept of the rate of change of water purification efficiency, and improves the accuracy of judging the downward trend of water purification efficiency through judgment of multiple consecutive cycles. The dynamically adjusted sampling frequency optimizes the use of energy and computing resources while ensuring the monitoring effect. 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 the subsequent adjustment of floating bed control strategies, which helps to achieve a better balance between improving water purification efficiency and protecting aquatic ecosystems.

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

[0097] In this regard, the present application further proposes that the method adopts specific steps when determining the execution time period with the lowest intensity of aquatic biological activity 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. In this way, the monitoring range and the number of time windows can be flexibly set to adapt to different application scenarios.

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

[0100] The monitoring time range is divided into N consecutive time windows according to the time window division parameter N. This division method can ensure that the length of each time window is equal, which is convenient for subsequent comparative analysis.

[0101] Next, the arithmetic mean of all aquatic biological activity intensity data in each time window is calculated. By calculating the average value, the fluctuation effect of a single data point can be eliminated, and a more stable and reliable activity intensity indicator can be obtained.

[0102] Finally, the average values ​​of the N time windows are compared, and the time window with the lowest average value is determined as the execution time period. This method can effectively identify the time period with the lowest overall intensity of aquatic biological activity, thereby minimizing the disturbance of floating bed flipping to the aquatic ecosystem.

[0103] This method can effectively smooth the impact of short-term fluctuations and obtain more reliable activity intensity assessment results by dividing the continuous time range into multiple time windows of equal length and calculating the average value of aquatic biological activity intensity in each window. By comparing the average values ​​of different time windows, the time period with the lowest aquatic biological activity intensity can be accurately identified, providing the best execution time for the floating bed flipping operation.

[0104] The advantage of this method is that it considers data over a longer time range, rather than relying solely on instantaneous data at a single time point. This can avoid misjudgments due to accidental factors and improve the accuracy and reliability of the execution time period selection. 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, a day can be divided into 6 4-hour time windows. The sampling interval can be set to 15 minutes, so that each time window will contain 16 data points. Assume that the aquatic biological activity intensity data collected in a certain time window (expressed after standardization) 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. The average activity intensity of the other 5 time windows is calculated in the same way, and the results are assumed to be 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 aquatic biological activity intensity.

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

[0107] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the 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 this regard, the present application further proposes that the method includes obtaining a preset water quality parameter monitoring cycle; obtaining a water quality purification efficiency adjustment coefficient and a control parameter adjustment step within the monitoring cycle; obtaining a preset control parameter priority order, wherein 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 a total control parameter adjustment amount; dividing the total control parameter adjustment amount by the control parameter adjustment step to obtain the number of adjustments; and adjusting the floating bed flipping frequency, flipping speed and floating bed height in turn within their respective preset adjustment ranges in order of priority.

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

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

[0111] Next, obtain the preset control parameter priority order. This application sets the floating bed flip frequency as the highest priority, because the flip frequency has the most direct and significant impact on water purification efficiency. The second is the flip speed, which affects the contact time between the plant and the water body. The floating bed height has the lowest priority because it is mainly used to adapt to water level changes and has relatively little impact on purification efficiency in the short term.

[0112] After calculating the difference between the water purification efficiency and the preset target efficiency, the difference is multiplied by the water purification efficiency adjustment coefficient to obtain the total adjustment amount of the control parameter. This step converts the efficiency difference into a specific adjustment amount, making subsequent adjustments more accurate. The total adjustment amount of the control parameter is divided by the control parameter adjustment step length to obtain the number of adjustments, which ensures the smoothness and controllability of the adjustment process.

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

[0114] As a preferred implementation, the water quality parameter monitoring cycle can be set to 4 hours, the water quality purification efficiency adjustment coefficient can be set to 0.8, and the control parameter adjustment step length can be set to 0.1. Assuming that the current water quality purification efficiency is 60% and the preset target efficiency is 80%, the efficiency difference is 20%. The difference is multiplied by the adjustment coefficient to obtain a total control parameter adjustment amount of 16%. The total adjustment amount is divided by the adjustment step length to obtain the number of adjustments of 1.6, which is rounded up to 2 times.

[0115] In this example, the frequency of the floating bed flipping is adjusted first. Assume that 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 1.1 times / hour. After the second adjustment, the flipping frequency becomes 1.2 times / hour. If the target efficiency is still not reached after the flipping frequency adjustment, the flipping speed is adjusted, and finally the height of the floating bed 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, the present application can minimize the interference to the aquatic ecosystem while ensuring the water purification efficiency. Compared with the prior art, the method of the present application achieves precise adjustment of the control parameters by introducing the water purification efficiency adjustment coefficient and the control parameter adjustment step, avoiding the problem of over-adjustment or under-adjustment. By setting the control parameter priority order, the system can flexibly adjust each parameter according to the actual situation to achieve the best purification effect. The present method can respond quickly according to the real-time changes in the water purification efficiency and adapt to different water quality conditions and environmental changes. By accurately controlling the adjustment amplitude and frequency, the interference to the aquatic ecosystem is minimized, and the balance between purification efficiency and ecological protection is achieved. The method can be easily extended to other types of water purification systems and has broad application prospects.

[0117] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the problem of how to accurately obtain the water purification efficiency adjustment coefficient and the control parameter adjustment step.

[0118] In this regard, the present application further proposes obtaining a water purification efficiency adjustment coefficient and a control parameter adjustment step.

[0119] The technical solution of the present application first obtains preset historical data collection parameters, including the number of historical monitoring cycles N and the benchmark adjustment coefficient. Then, water purification efficiency data of N historical monitoring cycles are continuously obtained, where N is an integer greater than or equal to 10. Then, the sum of squares of the deviations of the water purification efficiency data in the N historical monitoring cycles is calculated and divided by N-1 to obtain the variance. The square root operation is performed on the variance to obtain the standard deviation. The product of the standard deviation and the benchmark adjustment coefficient is obtained to obtain the water purification efficiency adjustment coefficient.

[0120] In addition, the present application also obtains the preset minimum adjustment times and maximum adjustment times. Within N historical monitoring cycles, the absolute value of the difference in water purification efficiency between two adjacent monitoring cycles is calculated, and the maximum value is selected as the maximum change. The maximum change is divided by the maximum adjustment times to obtain the control parameter adjustment step length.

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

[0122] First, by using data from multiple historical monitoring cycles, the changing trend of water purification efficiency can be more comprehensively reflected, avoiding the deviation that may be caused 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] Secondly, the calculation method of variance and standard deviation can effectively quantify the degree of dispersion 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. By multiplying the standard deviation with the benchmark adjustment coefficient, the water purification efficiency adjustment coefficient can be dynamically adjusted according to the actual data fluctuation.

[0124] Thirdly, by calculating the maximum value of the difference in water purification efficiency between adjacent monitoring cycles, the most extreme water quality changes can be captured. Dividing this maximum change by the maximum number of adjustments, the control parameter adjustment step size obtained can both cope with the maximum change and avoid overly drastic adjustments.

[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, you can set N=12 (that is, use the data of the most recent 12 monitoring cycles), and set the benchmark adjustment coefficient to 0.5.

[0127] 2. Collect historical data: Assume that the water purification efficiency data for 12 consecutive monitoring cycles 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 respectively.

[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 cycles: 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 accurately controlling the flipping frequency, flipping speed and height of the floating bed. This adaptive control strategy can better cope with the dynamic changes in water quality and improve the response speed and control 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 given to the most recent monitoring period to better reflect the current water quality change trend. The specific implementation method 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 cycle.

[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 purification efficiency adjustment coefficient = σw*baseline adjustment coefficient

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

[0148] In addition, the present application can also dynamically adjust the baseline adjustment coefficient according to seasonal changes or known periodic changes in water quality. For example, in seasons with more drastic changes in water quality, the baseline adjustment coefficient can be appropriately increased to enhance the regulation ability of the system. Conversely, in seasons with relatively stable water quality, the baseline adjustment coefficient can be reduced 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 control the ecological floating bed system more intelligently and accurately, thereby improving the water purification efficiency while minimizing the interference with the water ecosystem, thereby achieving a more efficient and environmentally friendly water pollution prevention and control effect.

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

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

[0152] Based on the acquired 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 absorbing heavy metals in the middle layer, and the third type of plants resistant to weak light in the bottom layer. This configuration method makes full use of the environmental characteristics of different water layers, allowing various types of plants to grow under the most suitable conditions.

[0153] In order to further improve the water purification effect, the present application also monitors the rhizosphere environmental parameters of the first, second and third plants. According to these parameters, the physical and chemical properties of the culture matrix are regulated to promote the formation of microbial communities. Through the synergistic effect of plant root secretions and microbial communities, efficient purification of pollutants is achieved.

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

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

[0156] Then, based on the pollutant data obtained, the system automatically selects the plant configuration that best suits the current water quality conditions from the preset plant database, which contains information on the purification capacity, growth characteristics, and environmental adaptability of various aquatic plants.

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

[0158] During the plant configuration stage, the system will select the first type of plants such as water hyacinth and water hyacinth that can efficiently absorb nitrogen and phosphorus based on the characteristics of sufficient light and suitable temperature in the upper layer. The middle layer will be equipped with second type of plants such as reeds and cattails that have strong heavy metal adsorption capabilities. The bottom layer has weak light, so the third type of plants such as foxtail algae and black algae that are resistant to weak light are selected.

[0159] In order to optimize the growth environment of plants, the present application also sets up a rhizosphere environment monitoring system. The system monitors parameters such as pH value, redox potential and organic matter content of the plant rhizosphere in real time through micro sensors. Based on these data, the system can accurately control the physical and chemical properties of the culture matrix to create favorable conditions for the formation of microbial communities.

[0160] For example, when the root pH value is detected to deviate from the target range, the system will automatically add an appropriate amount of buffer to adjust it. Similarly, if the organic matter content is insufficient, the system will add organic nutrients. This fine-grained regulation ensures that plant roots and microbial communities can work together in an optimal state, thereby improving water purification efficiency.

[0161] Through the above method, this application achieves precise control of complex water pollution. Compared with the traditional single-layer floating bed, 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 lower layer of the traditional floating bed is solved. In addition, the precise regulation of the rhizosphere environment further optimizes the efficiency of the plant-microorganism collaborative purification system.

[0162] As a preferred embodiment, the present application can be applied in an urban lake affected by multiple pollutants, including nitrogen, phosphorus and a small amount of heavy metals. Based on water quality monitoring data, the system chooses to configure water hyacinth and water hyacinth in the upper layer; reed and cattail in the middle layer; and foxtail and black algae in the bottom layer.

[0163] During the implementation process, the system monitors water quality and plant growth every 2 hours. When the rate of decrease of nitrogen and phosphorus concentrations in the upper layer is detected to be reduced, the system will automatically adjust the coverage ratio of water hyacinth and water hyacinth. At the same time, the planting density of reeds and cattails will be adjusted according to the changes in heavy metal concentrations in the middle layer. For the bottom layer, the system will adjust the planting depth of foxtail algae and black algae in a timely manner according to changes in light intensity.

[0164] Compared with the prior art, the multi-layer ecological floating bed water 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 purification efficiency. Secondly, 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 lower layers of the traditional floating bed. Thirdly, the precise regulation of the rhizosphere environment optimizes the plant-microorganism collaborative purification system and further improves the purification efficiency. Finally, this method can not only effectively remove pollutants from the water body, but also improve the overall health of the aquatic ecosystem, achieving the dual goals of water purification and ecological restoration.

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

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

[0167] The method first obtains the rhizosphere environmental 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. Next, the target parameter ranges required for the growth of the microbial community are obtained from a preset parameter database, including a target pH range, a target redox potential range, and a target organic matter content range. These target ranges are pre-set based on the optimal conditions for microbial growth.

[0168] According to the target parameter range, an adapted microbial strain is selected from a preset microbial strain database. This step ensures that the selected microbial strain can grow well under the target environmental conditions. Then, the rhizosphere environmental parameters are monitored according to the preset detection time interval. When the detected rhizosphere environmental parameters are not within the corresponding target range, a corresponding regulator is selected from a preset regulator database. The regulator includes a buffer for adjusting the pH value, a potential regulator for adjusting the redox potential, and an organic nutrient for supplementing organic matter.

[0169] The selected regulator is added to the culture matrix according to the preset addition amount. The rhizosphere environmental parameters are continuously monitored according to the preset detection time interval until the rhizosphere environmental parameters reach and stabilize within the corresponding target range. Finally, the adapted microbial strain is inoculated into the culture matrix to promote the formation of the microbial community.

[0170] The method of the present application creates optimal conditions for the formation of microbial communities by real-time monitoring and precise regulation of rhizosphere environmental parameters. Specifically, pH regulation can be achieved by adding a buffer, such as a phosphate buffer or a bicarbonate solution. Redox potential regulation can be achieved by adding an appropriate amount of an oxidant or a reductant, such as hydrogen peroxide or ascorbic acid. Organic matter content regulation can be achieved by adding organic nutrients, such as humic acid or an amino acid mixture.

[0171] Furthermore, the method of the present application can customize and regulate 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 absorb heavy metals, the pH value can be appropriately lowered and the redox potential can be increased to enhance the bioavailability of heavy metals.

[0172] Therefore, the method of the present application can not only accurately control the physical and chemical properties of the culture matrix, but also can be customized according to the characteristics of different plants and pollutants, thereby providing the best environmental conditions for the formation and growth of microbial communities. This refined control method helps to improve the water purification efficiency of the entire ecological floating bed system.

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

[0174] For example, suppose in an actual application scenario, the initial rhizosphere environment has a pH of 6.2, an ORP of 150mV, and an organic matter content of 2%. The target parameter ranges are pH 6.5-7.5, ORP 200-300mV, and organic matter content 3-5%. 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 ORP, and add humic acid to increase the organic matter content. The system will continue to monitor parameter changes until all parameters are stable within the target range.

[0175] This automated control method can not only improve the accuracy and efficiency of control, 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 control strategy and further improve the effect of microbial community formation.

[0176] The method of the present application has significant advantages over the prior art. Traditional culture matrix regulation methods often adopt fixed formulas or rough regulation 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 the present application also takes into account 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 the present application, there is still the problem of how to accurately calculate the water purification efficiency.

[0178] In this regard, the present application further proposes that the method takes into account multiple influencing factors, including pollutant concentration, plant coverage area, water level changes, etc., and uses a mathematical model to comprehensively evaluate the purification efficiency of the system.

[0179] Specifically, the water purification efficiency calculation method proposed in this application includes the following steps: taking the pollutant concentration in the water quality parameter as P(t), the plant coverage area in the plant growth condition 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] in,

[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 in hours;

[0186] P(t) represents the current pollutant concentration in mg / L;

[0187] Pmax represents the maximum treatable pollutant concentration in mg / L;

[0188] D(t) represents the degree of water disturbance, ranging from 0 to 1;

[0189] C(t) represents plant coverage, in m 2 ;

[0190] Cref represents the reference plant coverage, in m 2 ;

[0191] H(t) represents the current water level in m;

[0192] Href represents the reference water level, in m;

[0193] ΔR(t) represents the rate of change of the flip angle, in degrees / hour;

[0194] Rmax represents the maximum permissible flip angle, in degrees;

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

[0196] α represents the time attenuation 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 photoperiod influence coefficient, with a value range of 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 rollover disturbance attenuation coefficient, and its value range is 0.1-0.3.

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

[0202] The range of values ​​of these coefficients can adapt to different water quality conditions and environmental factors. For example, the range of values ​​of the basic purification efficiency coefficient k1 is 0.8-0.95, reflecting the basic purification capacity of the system. The range of values ​​of the time attenuation coefficient α is 0.01-0.05, indicating the degree of attenuation of system efficiency over time. The range of values ​​of the disturbance influence coefficient β is 0.1-0.3, reflecting the impact of water disturbance on purification efficiency.

[0203] The model also introduces a periodic function sin(2π*t / 24) to simulate the effect 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, which is used to adjust the degree of influence of water level changes on the purification efficiency. The value range of the overturning disturbance attenuation coefficient μ is 0.1-0.3, which reflects the disturbance effect of the floating bed overturning on the water body.

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

[0205] For example, in a specific embodiment, it is assumed that the parameters at a certain time t are as follows:

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

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

[0208] Water 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] Flip angle change rate ΔR(t) = 5 degrees / hour;

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

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

[0216] Selection coefficient:

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

[0218] Substituting these parameters into the model 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 E(t) value obtained by calculation is the water purification efficiency at that moment. This efficiency value takes into account multiple influencing factors and can more accurately reflect the actual purification situation.

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

[0224] First, this method takes into account multiple influencing factors, including pollutant concentration, plant coverage, water level changes, light cycle and floating bed turnover, which can more comprehensively reflect the complex situation in the actual water purification process.

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

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

[0227] Finally, this method takes into account the disturbance effect of the floating bed overturning on the water body. By introducing the overturning angle change rate and the overturning disturbance attenuation coefficient, it can more accurately evaluate the impact of the floating bed overturning on the purification efficiency.

[0228] Compared with the prior art, the water purification efficiency calculation method proposed in this application has obvious advantages. Traditional calculation methods usually only consider the changes in pollutant concentrations and ignore the influence of other important factors. The method of this application greatly improves the accuracy and adaptability of the calculation by introducing multiple influencing factors and adjustable coefficients. In addition, the method of this application also takes into account dynamic factors such as photoperiod and floating bed turnover, 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 purification.

[0229] Second, refer to Figure 2 The present application further proposes a water pollution prevention and control system based on an ecological floating bed, the system comprising:

[0230] Data acquisition module 210, used to obtain water quality parameters, plant growth status and water level change information;

[0231] The efficiency calculation module 220 is used to calculate the water purification efficiency according to the water quality parameters, plant growth conditions and water level change information;

[0232] The parameter determination module 230 is used to determine 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;

[0233] The execution control module 240 is used to control the floating bed to execute the flipping action of the flipping frequency and flipping speed and the adjustment action of the floating bed height to improve the water purification efficiency and minimize the interference to the aquatic ecosystem. The water pollution prevention and control system proposed in this application realizes 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, the water purification efficiency is calculated, and according to the difference between the purification efficiency and the target efficiency, the flipping frequency, flipping speed and height of the floating bed are dynamically adjusted, 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 a 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 of 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 the present application can perform any step of the above method.

[0236] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in 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 information on water quality parameters, plant growth conditions and water level changes; Calculating water purification efficiency based on 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 purification efficiency and the preset target efficiency; The floating bed is controlled 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 purification efficiency and minimize the interference with the aquatic ecosystem.

2. A water pollution prevention and control method based on an ecological floating bed according to claim 1, characterized in that: The steps 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 purification efficiency and minimize the interference with the aquatic ecosystem include: Obtain water quality parameters within the preset monitoring period; Calculate the change rate of water 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 purification efficiency is continuously decreasing, starting the aquatic organism activity monitoring device to obtain aquatic organism activity intensity data; Determine, based on the aquatic organism activity intensity data, an execution time period with the lowest aquatic organism activity intensity within a preset time range; During the execution period: Obtain the preset control parameter adjustment step amount; Adjust the flipping frequency, flipping speed and floating bed height in sequence according to the step amount; Obtaining adjusted water purification efficiency; When the water purification efficiency reaches the preset target efficiency, the current control parameters are kept unchanged.

3. A water pollution prevention and control method based on an ecological floating bed according to claim 2, characterized in that: When the water quality change trend shows that the water purification efficiency is continuously decreasing, the step of starting the aquatic biological activity monitoring device to obtain the aquatic biological activity intensity data includes: Obtain historical data on water purification efficiency within a preset monitoring period; Obtaining a preset threshold value of change rate of water purification efficiency; Based on the historical data, the ratio of the difference in water purification efficiency between adjacent time points to the time interval is calculated to obtain a water purification efficiency change rate; When the change rate is negative in three consecutive monitoring cycles and its absolute value exceeds the change rate threshold, it is determined that the water purification efficiency is continuously decreasing; Determine the sampling frequency of the aquatic biological activity monitoring device according to the correspondence between the absolute value of the change rate and the preset sampling frequency, wherein the greater the absolute value of the change rate, the higher the sampling frequency; Collect data on the intensity of aquatic biological activity according to the stated sampling frequency.

4. A water pollution prevention and control method based on an ecological floating bed according to claim 2, 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 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 preset sampling intervals; According to the time window division parameter N, the monitoring time range is equally divided into N continuous time windows; Calculate the arithmetic mean of all aquatic biological activity intensity data in each time window; The average values ​​of the N time windows are compared, and the time window with the lowest average value is determined as the execution time period.

5. The water pollution prevention and control method based on ecological floating bed according to claim 1 is characterized in 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 comprises: Get the preset water quality parameter monitoring cycle; Obtaining a water purification efficiency adjustment coefficient and a control parameter adjustment step length within the monitoring period; Obtain the preset control parameter priority order, where the floating bed flip frequency has the highest priority, followed by the flip speed, and the floating bed height has the lowest priority; Calculating the difference between the water purification efficiency and a preset target efficiency; Multiplying the difference by the water purification efficiency adjustment coefficient to obtain a total adjustment amount of the control parameter; Divide the total adjustment amount of the control parameter by the adjustment step of the control parameter to obtain the number of adjustments; According to the priority order, within the respective preset adjustment ranges, the floating bed flipping frequency, flipping speed and floating bed height are adjusted in sequence, wherein: When adjusting the floating bed flipping frequency, the amplitude of each adjustment is the control parameter adjustment step and does not exceed the preset maximum flipping frequency; When adjusting the flipping speed, the amplitude of each adjustment is the control parameter adjustment step and does not exceed the preset maximum flipping speed; When adjusting the height of the floating bed, the amplitude of each adjustment is the control parameter adjustment step and does not exceed the preset maximum adjustment height.

6. The water pollution prevention and control method based on ecological floating bed according to claim 5 is characterized in that: The step of obtaining the water purification efficiency adjustment coefficient and the control parameter adjustment step length within the monitoring period includes: Obtain preset historical data collection parameters, including the number of historical monitoring cycles N and the benchmark adjustment coefficient; Continuously obtain water purification efficiency data for N historical monitoring cycles, where N is an integer greater than or equal to 10; Calculate the sum of squares of the deviations of the water purification efficiency data in the N historical monitoring periods, and divide it by N-1 to obtain the variance; Performing a square root operation on the variance to obtain a standard deviation; The water purification efficiency adjustment coefficient is obtained by multiplying the standard deviation by the benchmark adjustment coefficient; Get the preset minimum and maximum adjustment times; In the N historical monitoring cycles, the absolute value of the difference in water purification efficiency between two adjacent monitoring cycles is calculated, and the maximum value thereof is selected as the maximum change amount; The maximum change is divided by the maximum number of adjustments to obtain the control parameter adjustment step.

7. A multi-layer ecological floating bed water purification method according to claim 1, characterized in that: The method further comprises: Obtain the concentration data and type information of various pollutants in the water body from the preset water quality monitoring system; According to the concentration data and type information of the pollutants, a corresponding plant configuration scheme is obtained from a preset plant database; Divide the floating bed into the upper layer, middle layer and bottom layer, and obtain the light intensity, temperature and dissolved oxygen data of each layer; Arrange a first type of plant for absorbing nitrogen and phosphorus on the upper layer according to the plant arrangement scheme and the light intensity, temperature and dissolved oxygen data of the upper layer; According to the plant configuration scheme and the light intensity, temperature and dissolved oxygen data of the middle layer, a second type of plant for adsorbing heavy metals is configured in the middle layer; According to the plant configuration scheme and the light intensity, temperature and dissolved oxygen data of the bottom layer, a third type of plant that is tolerant to weak light is configured on the bottom layer; monitoring rhizosphere environmental parameters of the first type of plants, the second type of plants, and the third type of plants; Regulating the physical and chemical properties of the culture matrix according to the rhizosphere environmental parameters to promote the formation of microbial communities; The pollutants are purified through the synergistic effect of the root secretions of the first type of plants, the second type of plants and the third type of plants and the microbial community.

8. A multi-layer ecological floating bed water purification method according to claim 7, characterized in that: The step of regulating the physical and chemical properties of the culture matrix according to the rhizosphere environmental parameters to promote the formation of the microbial community comprises: Obtaining rhizosphere environmental parameters of the first type of plants, the second type of plants, and the third type of plants, wherein the rhizosphere environmental parameters include pH value, redox potential, and organic matter content; Obtaining target parameter ranges required for the growth of microbial communities from a preset parameter database, including a target pH range, a target redox potential range, and a target organic matter content range; Selecting a suitable microbial strain from a preset microbial strain database according to the target parameter range; Monitoring the rhizosphere environmental parameters according to a preset detection time interval; When the detected rhizosphere environmental parameters are not within the corresponding target range, a corresponding regulator is selected from a preset regulator database, and the regulator includes: Buffering agents for adjusting pH, potential regulators for adjusting redox potential, and organic nutrients for supplementing organic matter; adding the selected regulator to the culture matrix according to a preset addition amount; Continue to monitor the rhizosphere environmental parameters according to the preset detection time interval until the rhizosphere environmental parameters reach and stabilize within the corresponding target range; The adapted microbial strain is inoculated into a culture matrix to promote the formation of a microbial community.

9. The water pollution prevention and control method based on ecological floating bed according to claim 1 is characterized in that: The step of calculating the water purification efficiency according to the water quality parameters, plant growth conditions and water level change information specifically includes: The pollutant concentration in the water quality parameter is taken as P(t), the plant coverage area in the plant growth condition is taken as C(t), and the water level change information is taken as H(t), and the water purification efficiency is calculated by substituting into the following mathematical model: 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)] in, 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 in hours; P(t) represents the current pollutant concentration in mg / L; Pmax represents the maximum treatable pollutant concentration in mg / L; D(t) represents the degree of water disturbance, ranging from 0 to 1; C(t) represents plant coverage, in m 2 ; Cref represents the reference plant coverage, in m 2 ; H(t) represents the current water level in m; Href represents the reference water level, in m; ΔR(t) represents the rate of change of the flip angle, in degrees / hour; Rmax represents the maximum permissible flip angle, in degrees; k1 represents the basic purification efficiency coefficient, and its value range is 0.8-0.95; α represents the time attenuation 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 photoperiod influence coefficient, with a value range of 0.1-0.2; λ represents the water level influence coefficient, and its value range is 0.2-0.4; μ represents the rollover disturbance attenuation coefficient, and its value range is 0.1-0.

3.

10. A water pollution prevention and control system based on an ecological floating bed, characterized in that: The system includes: Data acquisition module, used to obtain water quality parameters, plant growth status and water level change information; An efficiency calculation module, used to calculate the water purification efficiency according to the water quality parameters, plant growth conditions and water level change information; A parameter determination module, used to determine 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; The execution control module is used to control the floating bed to execute the flipping action of the flipping frequency and flipping speed and the adjustment action of the floating bed height, so as to improve the water purification efficiency and minimize the interference to the aquatic ecosystem.

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