Artificial reef microorganism load optimization method and system and storage medium

Abstract

The invention relates to the technical field of ecological engineering, and discloses an artificial reef microorganism load optimization method and system and a storage medium. The method comprises the following steps: placing submerged plant seedlings, algicidal bacteria liquid and competitive algae liquid into a sodium alginate gel matrix, and carrying out calcium ion crosslinking to form a ternary symbiont unit; loading the chitosan-gelatin into a chitosan-gelatin spherical carrier according to a volume ratio to prepare a layered release carrier; establishing a dynamic equation, and calculating an optimal putting parameter by using a self-adaptive control algorithm; dividing water area grids, setting monitoring nodes, and solving an optimal putting scheme of each grid according to the optimal parameters and the target function; and collecting water quality data of each monitoring node, calculating a tracking error with expected water quality, and updating a parameter matrix to dynamically adjust load parameters. According to the method for constructing the collaborative system, the problems that in the prior art, the survival rate of microorganisms is low, the feeding precision is insufficient, ecological collaboration is lacked, and the dynamic adjusting capacity is insufficient are solved, and the accuracy and the long-term effect of algae bloom treatment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological engineering, and in particular to a method and system for optimizing microbial loading of artificial reefs and a storage medium. BACKGROUND

[0002] Currently, the phenomenon of algal blooms caused by eutrophication of water bodies has become a global environmental problem. Traditional algal bloom control technologies mainly include physical methods (such as mechanical salvage and aeration oxygenation), chemical methods (such as adding chemical agents), and biological methods (such as adding microbial preparations and planting aquatic plants). Among them, the biological method has attracted widespread attention due to its environmental friendliness and sustainability. Existing microbial control technologies usually use the method of directly adding algicidal bacteria or competitive microorganisms to control algal blooms by decomposing harmful algae or competing for nutrients through the metabolic activities of microorganisms. Artificial reefs, as an important carrier for ecological restoration, provide attachment and habitat for microorganisms and play an important role in water environment management.

[0003] However, the existing technology has significant shortcomings: first, the survival rate of microorganisms is low and the effect is not lasting. Traditional direct delivery methods cannot provide a suitable living environment for microorganisms, resulting in the death or inactivation of a large number of beneficial bacteria in a short period of time after delivery. Second, the delivery precision is insufficient, lacking precise spatial distribution control and time release control, resulting in uneven treatment effect and resource waste. Third, there is a lack of systematic ecological synergy design, and the effect of single microorganisms is limited, which cannot form a stable ecological cycle. Fourth, the monitoring feedback mechanism is imperfect, which cannot grasp the treatment effect in real time and adjust the strategy in time, resulting in a lack of dynamic adaptation ability in the treatment process. SUMMARY

[0004] The present application provides a method and system for optimizing microbial loading of artificial reefs and a storage medium, which solves the problems of low survival rate of microorganisms, insufficient delivery precision, lack of ecological synergy, and insufficient dynamic adjustment ability in the prior art, and improves the precision and long-term effectiveness of algal bloom control.

[0005] In a first aspect, the present application provides a method for optimizing microbial loading of artificial reefs, which comprises: Step S101, placing the submerged plant seedlings, algicidal bacteria liquid, and competitive algal liquid into sodium alginate gel matrix together to form a ternary symbiotic unit through calcium ion cross-linking reaction; Step S102, filling the ternary symbiotic unit into a spherical carrier constructed of chitosan-gelatin composite material at a volume ratio of 1:2:1 to prepare a layered release carrier with an outer fast-dissolving layer, a middle slow-release layer, and an inner long-acting layer; Step S103, the system state vector includes the dynamic equation of harmful algae concentration, algicidal bacteria concentration and competitive algae concentration, and the optimal parameter combination of the release amount, release interval time and release depth of the layered release carrier in the control input vector is calculated through an adaptive control algorithm; Step S104, the target water area is divided into grid cells and monitoring nodes are set, the release scheme of the layered release carrier in each grid is determined according to the optimal parameter combination, and a spatial optimization target function is established to solve the optimal release amount and depth distribution of each grid; Step S105, the chlorophyll a concentration, dissolved oxygen concentration and pH value data of each monitoring node are collected, the tracking error of the expected water quality index and the actual monitoring value is calculated, and the adaptive parameter matrix is updated according to the tracking error to realize dynamic adjustment of the load parameters of the layered release carrier.

[0006] Optionally, the step S101 comprises: The root system part of the submerged plant seedling is immersed in a composite culture medium containing algicidal bacteria liquid and competitive algae liquid, and a plant root system carrier is formed by immersion treatment; A sodium alginate gel matrix is added to the composite culture medium, and the plant root system carrier is placed in the sodium alginate gel matrix for calcium ion crosslinking reaction to obtain a solidification embedding structure; Based on the circulation mechanism that the organic acid and amino acid secreted by the root system of the submerged plant provide nutrient sources for the algicidal bacteria in the solidification embedding structure, a synergistic relationship among the submerged plant, the algicidal bacteria and the competitive algae is established to obtain a synergistic ecological body; The stability of the synergistic ecological body is verified, and the proliferation state of the algicidal bacteria and the activity state of the competitive algae are measured, and when the algicidal bacteria and the competitive algae both reach stable activity states, the ternary symbiotic unit is obtained.

[0007] Optionally, the step S102 comprises: The ternary symbiotic unit is allocated according to the volume ratio of the submerged plant, the algicidal bacteria and the competitive algae to obtain an allocation unit; A spherical carrier matrix is constructed based on the molecular weight of chitosan and the concentration of gelatin, the allocation unit is loaded into the spherical carrier matrix to obtain a loaded carrier; The surface of the loaded carrier is designed as a three-layer structure of an outer fast-dissolving layer for releasing competitive algae, a middle slow-release layer for releasing algicidal bacteria and an inner long-acting layer for protecting submerged plants to obtain a multi-layer structure carrier; The degradation rates of each layer are controlled according to the dissolution time sequence of the multi-layer structure carrier, the outer layer is completely dissolved in a short time, the middle layer is gradually degraded, and the inner layer is slowly degraded to obtain the layered release carrier.

[0008] Optionally, the step S103 comprises: construct a system state vector based on the harmful algae concentration, the algae-lysing bacteria concentration, and the competitive algae concentration, perform dynamic analysis and processing on the system state vector, and obtain a state change rule; construct a control input vector based on the layered release carrier release quantity, release interval time, and release depth, perform control relationship establishment on the control input vector in association with the state change rule, and obtain a control strategy; input the control strategy into an adaptive control algorithm to perform parameter optimization calculation, solve control parameters through adaptive parameter adjustment and basis function calculation, and obtain adaptive control parameters; perform optimization calculation on the release quantity, release interval time, and release depth in the control input vector based on the adaptive control parameters, solve optimal values of each parameter, and obtain the optimal parameter combination.

[0009] Optionally, the step of constructing a system state vector based on the harmful algae concentration, the algae-lysing bacteria concentration, and the competitive algae concentration, performing dynamic analysis and processing on the system state vector, and obtaining a state change rule comprises: perform vector construction by combining and arranging the harmful algae concentration value, the algae-lysing bacteria concentration value, and the competitive algae concentration value, and obtain a concentration data vector; perform time sequence sampling on the concentration data vector, arrange concentration data vectors at different time points in chronological order, and obtain time sequence state data; calculate the change trend and change rate of each concentration parameter based on the time sequence state data, perform data difference calculation and trend analysis processing, and obtain concentration change characteristics; input the concentration change characteristics into a dynamic analysis module to perform rule identification, determine a system state evolution path through pattern matching and trend prediction calculation, and obtain the state change rule.

[0010] Optionally, the step S104 comprises: perform grid division processing on the target water area according to water area area, set monitoring nodes at grid intersection points, and obtain a grid monitoring layout; perform release scheme configuration on each grid unit based on the release quantity, release interval time, and release depth parameters in the optimal parameter combination, and obtain an initial release configuration; input the algae bloom concentration, release cost, and treatment effect of each grid unit into a spatial optimization objective function to perform weight calculation, calculate the release priority and resource allocation proportion of each grid through objective function solving calculation, and obtain a spatial distribution strategy; perform optimization adjustment on the initial release configuration according to the spatial distribution strategy, calculate the final layered release carrier release quantity and release depth value of each grid, and obtain the optimal release quantity and depth distribution of each grid.

[0011] Optionally, the step S105 comprises: Based on the chlorophyll a sensor, dissolved oxygen sensor, pH sensor of each monitoring node, data collection is performed, and the collected chlorophyll a concentration, dissolved oxygen concentration, and pH value data are processed to obtain real-time water quality monitoring data; The real-time water quality monitoring data is differentially calculated with the preset expected water quality index value, and the deviation of each water quality parameter is obtained through numerical subtraction operation to obtain tracking error data; Based on the tracking error data, the error change rate and the cumulative error are calculated, the error data is input into the adaptive parameter updating algorithm for parameter matrix adjustment calculation, and the updated adaptive parameter matrix is obtained; According to the updated adaptive parameter matrix, the release quantity, release interval time and release depth parameters of the layered release carrier are recalculated, the load parameters are dynamically corrected, and the adjusted load parameter configuration is obtained.

[0012] In a second aspect, the present application provides an artificial reef microbial load optimization system, comprising: A symbiotic body construction module is configured to place the submerged plant seedlings, the algicidal bacteria liquid, and the competitive algal liquid into a sodium alginate gel matrix together, and form a ternary symbiotic body unit through a calcium ion cross-linking reaction; A carrier preparation module is configured to load the ternary symbiotic body unit into a chitosan-gel spherical carrier at a volume ratio of 1:2:1, to prepare a layered release carrier with a fast-dissolving outer layer, a slow-release middle layer, and a long-acting inner layer; An optimal parameter calculation module is configured to establish a system state vector dynamic equation containing the concentrations of harmful algae, algicidal bacteria, and competitive algae, and calculate the optimal parameter combination of the release quantity, interval, and depth of the layered release carrier through an adaptive control algorithm; A release scheme optimization module is configured to divide a target water area into grids and set monitoring nodes, determine the release scheme of each grid according to the optimal parameters, and build a space optimization objective function to obtain the optimal release quantity and depth distribution of each grid; A parameter adjustment module is configured to collect chlorophyll a, dissolved oxygen, and pH data of the monitoring nodes, calculate the tracking error of the expected and actual water quality, update the adaptive parameter matrix according to the error, and dynamically adjust the load parameters of the layered release carrier.

[0013] In a third aspect, an artificial reef microbial load optimization device is provided, comprising a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory, so that the artificial reef microbial load optimization device executes the artificial reef microbial load optimization method described above.

[0014] In a fourth aspect, a computer readable storage medium is provided, which stores instructions that, when executed on a computer, cause the computer to perform the artificial reef microbial load optimization method described above.

[0015] In the technical scheme provided in the present application, the submerged plant seedlings, the algicidal bacteria liquid and the competitive algae liquid are placed in the sodium alginate gel matrix to form a ternary symbiotic unit through a calcium ion cross-linking reaction, and a stable biological synergistic system is constructed. The organic acids and amino acids secreted by the root system of the submerged plant provide a nutrient source for the algicidal bacteria, forming a self-sustaining nutrient circulation mechanism, which significantly improves the survival rate and activity stability of the microorganisms, and solves the problem of death and inactivation of the microorganisms in the prior art in a short period of time. The ternary symbiotic unit is filled into a layered release carrier with an outer layer of a fast-dissolving layer, a middle layer of a slow-release layer and an inner layer of a long-acting layer in a volume ratio, realizing time-sequential microbial release control. The outer layer releases the competitive algae quickly to inhibit in the early stage, the middle layer slowly releases the algicidal bacteria to maintain the sustained effect, and the inner layer protects the submerged plant to provide ecological restoration, avoiding the resource waste and environmental impact caused by traditional one-time release, and greatly improving the effective utilization rate of the microbial load and the sustainability of the treatment effect.

[0016] A system state vector containing harmful algae concentration, algicidal bacteria concentration and competitive algae concentration is established, and the optimal parameter combination of the layered release carrier release quantity, release interval time and release depth is calculated through an adaptive control algorithm, so that the algorithm can dynamically adjust the control strategy according to the real-time changes of the water quality state, overcoming the limitations of the prior art that fixed parameters cannot adapt to environmental changes. The target water area is divided into grid units, and a spatial optimization objective function is established according to the optimal parameter combination to solve the optimal release quantity and depth distribution of each grid, realizing precise spatial distribution control and differentiated treatment strategies, which significantly improves the treatment efficiency compared with the extensive release of the prior art. The chlorophyll a concentration, dissolved oxygen concentration and pH value data of each monitoring node are collected and tracked error is calculated to update the adaptive parameter matrix, forming a complete closed-loop feedback control system, so that the system can learn and optimize independently according to the treatment effect, realizing intelligent dynamic adjustment of the load parameters. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 The flowchart of the artificial reef microbial load optimization method in the present application is shown in the figure. Figure 2 For the flowchart of the input vector optimization process in the present application, the input vector includes the number of releases, the interval time of releases, and the release depth; Figure 3 For the structural diagram of the artificial reef microbial load optimization system in the present application; Figure 4 For the structural diagram of the artificial reef microbial load optimization system in the present application; DETAILED DESCRIPTION

[0019] The embodiments of the present application provide an artificial reef microbial load optimization method, system and storage medium. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0020] For the convenience of understanding, the specific flow of the embodiments of the present application is described below, please refer to Figure 1 An embodiment of the artificial reef microbial load optimization method in the present application includes: Step S101, the submerged plant seedlings, the algicidal bacteria bacterial solution and the competitive algal solution are placed in the sodium alginate gel matrix together to form a ternary symbiotic unit through calcium ion cross-linking reaction.

[0021] Specifically, the root system part of the submerged plant seedling is immersed in a composite culture medium containing the bacterium liquid and the competitive algal liquid, the root system is fully contacted with the two bacterium liquids through the immersion treatment, a plant root system carrier is formed, and a foundation is laid for subsequent microbial attachment and symbiosis. Then, sodium alginate gel matrix is added to the composite culture medium, and the plant root system carrier is placed in the matrix to perform a calcium ion cross-linking reaction, so that the gel matrix is solidified to form a solidified embedding structure wrapping the root system, the bacterium and the competitive algae, and providing a stable living environment for the three. Subsequently, based on the characteristics of the submerged plant root system in the solidified embedding structure that can secrete organic acids and amino acids, these secretions can be used as a nutrient source for the bacterium, and a nutrient circulation and synergistic relationship among the submerged plant, the bacterium and the competitive algae is constructed to form a synergistic ecological body. Finally, the stability of the synergistic ecological body is verified, and when the proliferation state of the bacterium and the activity state of the competitive algae are both stable, a ternary symbiotic body unit is obtained.

[0022] In step S102, the ternary symbiotic body unit is filled into a spherical carrier constructed of a chitosan-gelatin composite material at a volume ratio of 1:2:1 to prepare a layered release carrier having an outer fast-dissolving layer, a middle slow-release layer and an inner long-acting layer.

[0023] Specifically, the ternary symbiotic body unit is distributed according to the volume ratio of 1:2:1 of the submerged plant, the bacterium and the competitive algae to ensure that the proportions of the three meet the subsequent action requirements, and a proportioned distribution unit is obtained. Then, according to the characteristics of the chitosan molecular weight and the gelatin concentration, a spherical carrier substrate is constructed, the proportioned distribution unit is filled into the spherical carrier substrate, a filled carrier is formed, and the ternary symbiotic body unit has a bearing structure. Then, the surface of the filled carrier is designed in layers, the outer layer is a fast-dissolving layer for subsequent rapid release of the competitive algae to achieve initial algae control, the middle layer is a slow-release layer for slow release of the bacterium to ensure long-term algae control effect, and the inner layer is a long-acting layer for protecting the root system of the submerged plant to help its subsequent planting and growth, thereby obtaining a multi-layer structure carrier. Finally, according to the dissolution time sequence requirements of the multi-layer structure carrier, the degradation rates of the layers are controlled to make the outer layer completely dissolved in a short time, the middle layer gradually degraded, and the inner layer slowly degraded, and finally a layered release carrier is prepared.

[0024] In step S103, a dynamic equation containing the harmful algal concentration, the bacterium concentration and the competitive algal concentration is established for a system state vector, and an optimal parameter combination of the number, the interval time and the depth of the layered release carrier in the control input vector is calculated by an adaptive control algorithm.

[0025] Specifically, the harmful algae concentration, the algae-lysing bacteria concentration, and the competitive algae concentration are taken as core indexes to construct a system state vector. By dynamically analyzing the vector, the changes of the three biological concentrations over time are observed, the state change rule is summarized, and the dynamic trend of the current water ecological environment is determined. According to the three key release parameters of the release quantity, the release interval, and the release depth of the layered release carrier, a control input vector is constructed. The control input vector is associated with the state change rule obtained before, the influence of different release parameters on the change of the biological concentration is analyzed, and a corresponding control strategy is established. The control strategy is input into the adaptive control algorithm, and the adaptive control parameters that adapt to the current water conditions are solved through the adaptive parameter adjustment and the basis function calculation in the algorithm. Finally, based on the adaptive control parameters, the release quantity, the release interval, and the release depth in the control input vector are optimized to determine the optimal values of the parameters, and the optimal parameter combination is obtained.

[0026] In step S104, the target water area is divided into grid cells and monitoring nodes are set, the release scheme of the layered release carrier in each grid is determined according to the optimal parameter combination, and a spatial optimization objective function is established to solve the optimal release quantity and depth distribution of each grid. Specifically, according to the size of the target water area, the target water area is divided into a plurality of grid cells, and monitoring nodes are set at the intersection points of each grid to form a grid monitoring layout. Then, based on the optimal parameter combination, the initial state of each grid cell is combined to preliminarily configure the release scheme of the layered release carrier in each grid cell, determine the initial release quantity, interval, and depth, and obtain the initial release configuration. The algal bloom concentration (reflecting the pollution degree), the release cost (reflecting the resource consumption), and the treatment effect (reflecting the expected effect) of each grid cell are taken as inputs, and are substituted into the spatial optimization objective function for weight calculation. The release priority and resource allocation ratio of each grid are determined by function solving, and it is clear which grid needs to be focused on and which grid can reduce the release, so as to form a spatial distribution strategy. Finally, the initial release configuration is optimized and adjusted according to the spatial distribution strategy, the final release quantity and depth of the layered release carrier in each grid are recalculated, and the optimal release quantity and depth distribution of each grid are obtained.

[0027] In step S105, the chlorophyll a concentration, the dissolved oxygen concentration, and the pH value data of each monitoring node are collected, the tracking error between the expected water quality index and the actual monitoring value is calculated, and the adaptive parameter matrix is updated according to the tracking error to realize dynamic adjustment of the load parameters of the layered release carrier.

[0028] Specifically, the chlorophyll a sensor, dissolved oxygen sensor, and pH sensor equipped on each monitoring node are used to collect the chlorophyll a concentration (reflecting the algal bloom situation), dissolved oxygen concentration (reflecting the aerobic environment of the water body), and pH value (reflecting the acidity and alkalinity of the water body) data of the corresponding position, respectively. The collected data is summarized to obtain real-time water quality monitoring data. Then, the real-time water quality monitoring data is compared with the preset expected water quality index value, and the deviation of each water quality parameter, i.e., the tracking error data, is obtained by calculating the difference between the two, so as to understand the gap between the current water quality and the target water quality. Then, based on the tracking error data, the error change rate and the cumulative error are further calculated, and these error related data are input into the adaptive parameter updating algorithm to adjust the adaptive parameter matrix through algorithm operation, and the updated adaptive parameter matrix is obtained. Finally, according to the updated adaptive parameter matrix, the release quantity, release interval time, and release depth parameters of the layered release carrier are recalculated, the original load parameters are dynamically corrected, and the adjusted load parameter configuration is obtained.

[0029] It can be understood that the execution subject of the present application can be an artificial reef microbial load optimization system, and can also be a terminal or a server, which is not limited here. The server is taken as an example for description in the embodiments of the present application.

[0030] In a specific embodiment, the process of executing step S101 can specifically include the following steps: (1) Immersing the root system part of the submerged plant seedling into a composite culture medium containing algicidal bacteria liquid and competitive algal liquid to form a plant root system carrier through immersion treatment; (2) Adding sodium alginate gel matrix to the composite culture medium, and placing the plant root system carrier in the sodium alginate gel matrix to perform calcium ion crosslinking reaction to obtain a solidified embedding structure; (3) Based on the circulation mechanism that the organic acid and amino acid secreted by the root system of the submerged plant provide nutrient sources for the algicidal bacteria, a synergistic relationship among the submerged plant, the algicidal bacteria, and the competitive algae is established to obtain a synergistic ecological body; (4) Verifying the stability of the synergistic ecological body, and measuring the proliferation state of the algicidal bacteria and the activity state of the competitive algae. When the algicidal bacteria and the competitive algae both reach the stable activity state, the ternary symbiotic unit is obtained.

[0031] Specifically, when the root system part of the submerged plant seedling is immersed into the composite culture medium containing algicidal bacteria liquid and competitive algal liquid, the composite culture medium is prepared by mixing LB culture medium and BG11 culture medium at a volume ratio of 1:1. The concentration of the algicidal bacteria liquid is determined by plate counting method, 1 mL of the liquid is gradiently diluted to 10 -6 times, 0.1 mL of the diluted liquid is spread on LB solid culture medium, and the number of colonies is counted after 37°C culture for 24 hours. The concentration of the algicidal bacteria liquid is calculated according to the formula The concentration was calculated to be 1 x 10 8 CFU / mL; the competitive algal cell density was counted by a hemocytometer, and the total number of cells in five central squares was counted under an optical microscope, and the formula was used The concentration was calculated to be 1 x 10 6 The infiltration treatment time was set to 48 hours, and the temperature of the culture medium was controlled at 25°C ± 0.5°C by using a constant temperature incubator, and the pH value was adjusted to 7.0 ± 0.1 by using 0.1 mol / L hydrochloric acid or sodium hydroxide solution. In this process, the algicidal bacteria adhered to the root surface polysaccharide mucus to form a biofilm with a thickness of about 20-30 μm, and the competitive algae formed microcolonies around the roots within a range of 1-2 mm by active movement, together forming a plant root carrier that provided an initial adhesion environment for the microorganisms and alleviated the problem of the loss of the microorganisms due to the lack of adhesion points when directly injected.

[0032] A sodium alginate gel matrix with a mass concentration of 2% was added to the composite culture medium, and a magnetic stirrer was used to continuously stir at a speed of 150 r / min for 30 minutes to ensure that the sodium alginate was completely dissolved and uniformly mixed with the culture medium. Subsequently, the plant root carrier was completely placed into the mixed system, and 0.1 mol / L calcium chloride solution was slowly added by using a peristaltic pump, and the dropwise addition rate was controlled to be 1 drop / s, and the stirring rate was maintained at 50 r / min during the dropwise addition process, and the calcium ion crosslinking reaction was carried out at 25°C in a constant temperature environment, and the reaction lasted for 6 hours. After the reaction was completed, the gel matrix formed a porous structure with a pore size of 50-100 μm, which wrapped the submerged plant roots, algicidal bacteria and competitive algae, and a solidified embedding structure was obtained. This structure can avoid the direct exposure of the microorganisms to the water body and the influence of environmental factors such as water flow impact and temperature fluctuations, and solves the problem of the loss of the microorganisms due to the lack of protection when directly injected in the prior art.

[0033] Based on the solidified embedding structure, the submerged plant roots secrete organic acids such as citric acid (concentration of about 50 μmol / L), malic acid (concentration of about 30 μmol / L), and amino acids such as glutamic acid (concentration of about 20 μmol / L) and aspartic acid (concentration of about 15 μmol / L), and these secretions provide carbon and nitrogen sources for the algicidal bacteria through diffusion, supporting the proliferation of the algicidal bacteria; the algicidal bacteria secrete protein and polypeptide algicidal substances during the proliferation process to inhibit the growth of harmful algae; and the competitive algae compete with the harmful algae for nutrients such as nitrate and phosphate in the water body, forming a synergistic cycle of “plant energy supply-microbial algae control”, and constructing a synergistic ecological body.

[0034] Based on their solidified and embedded structures, submerged plant roots continuously secrete organic acids such as citric acid (approximately 50 μmol / L) and malic acid (approximately 30 μmol / L), as well as amino acids such as glutamic acid (approximately 20 μmol / L) and aspartic acid (approximately 15 μmol / L). These secretions serve as carbon and nitrogen sources for algicidal bacteria through diffusion, supporting their proliferation. Simultaneously, the algicidal proteins secreted by these bacteria during proliferation (approximately 10 μg / mL) can disrupt the cell walls of harmful algae, while competing algae compete with harmful algae for NO3- in the water. - -N (absorption rate approximately 0.5 mg / (L・d)) and PO4 3- -P (absorption rate approximately 0.08 mg / (L・d)) forms a synergistic mechanism of "submerged plant-alginolytic bacteria-competing algae". The OD600 value of the alginolytic bacteria was measured at 600 nm using a UV spectrophotometer, where OD600 refers to the optical density (absorbance) measured at 600 nm. When the OD600 value stabilized at 0.6 ± 0.05, it indicated that the concentration of the alginolytic bacteria had reached a stable state. The cell density of the competing algae was counted under an optical microscope using a hemocytometer. When the cell density stabilized at 1 × 10⁻⁶, the concentration of the competing algae was considered stable. 6 Cells / mL ± 5 × 10 4 When the concentration of cells / mL was reached, a stable synergistic relationship was confirmed among the three, resulting in a synergistic ecosystem.

[0035] To verify the stability of the synergistic ecosystem, samples were taken daily at 9:00 AM, with a sample volume of 5 mL. The samples were serially diluted 10 times using the plate count method. 4 After doubling, 0.1 mL was spread onto LB solid medium and incubated at 37℃ for 24 hours. The number of algicidal bacteria colonies per unit volume was then counted. The chlorophyll a content of the competing algae was measured at a wavelength of 680 nm using a spectrophotometer. The calculation method was chlorophyll a content (mg / L) = 11.64 × (OD680 - OD750) - 2.16 × (OD630 - OD750) + 0.10 × (OD480 - OD750), where OD680, OD750, OD630, and OD480 refer to the optical density (absorbance value) measured at wavelengths of 680 nm, 750 nm, 630 nm, and 480 nm, respectively. When the fluctuation range of the algicidal bacteria colony count was less than 5% for 7 consecutive days, and the chlorophyll a content of the competing algae remained in the range of 0.8–1.2 mg / L, it was determined that both the algicidal bacteria and the competing algae had reached a stable active state, and at this time, the ternary symbiotic unit was obtained. This unit provides a continuous supply of nutrients to microorganisms through internal nutrient circulation, preventing them from dying due to lack of nutrients and solving the problems of low microbial survival rate and short-lasting effects in existing technologies.

[0036] In one specific embodiment, the process of performing step S102 may specifically include the following steps: (1) The ternary symbiotic unit is allocated according to the volume ratio of submerged plants, algicidal bacteria and competitive algae to obtain a ratio allocation unit; (2) A spherical carrier matrix is constructed based on the molecular weight of chitosan and the concentration of gelatin, and the ratio allocation unit is loaded into the spherical carrier matrix to obtain a loaded carrier; (3) The surface of the loaded carrier is designed with a three-layer structure of an outer fast-dissolving layer for releasing competitive algae, a middle slow-release layer for releasing algicidal bacteria, and an inner long-acting layer for protecting submerged plants, to obtain a multi-layer structure carrier; (4) The degradation rates of each layer are controlled according to the dissolution time sequence of the multi-layer structure carrier, the outer layer completely dissolves in a short time, the middle layer gradually degrades, and the inner layer slowly degrades, to obtain the layered release carrier.

[0037] Specifically, the ternary symbiotic unit is allocated according to the volume ratio of 1:2:1 of submerged plants, algicidal bacteria and competitive algae, a length of 2-3 cm of submerged plant root segments (single volume of about 0.5 mL) is accurately measured using a pipette, 1 mL of algicidal bacteria liquid with a concentration of 1×10 8 CFU / mL and 0.5 mL of competitive algae liquid with a cell density of 1×10 6 are aspirated through sterile operation, and the three are placed in a sterile centrifuge tube and mixed gently to obtain a ratio allocation unit. The volume ratio is determined according to the ecological function requirements of the three, the highest proportion of algicidal bacteria can ensure that it reaches an effective algicidal concentration in the water body, and the proportions of submerged plants and competitive algae can ensure the stable play of ecological synergy, solving the problem of lack of systematic ecological synergy design in the prior art.

[0038] A spherical carrier matrix is constructed based on the molecular weight of chitosan (80000 Da) and a gelatin solution with a mass concentration of 8%, chitosan powder is dissolved in a 1% acetic acid solution, stirred until completely dissolved to form a chitosan solution with a mass concentration of 5%, gelatin powder is dissolved in deionized water, stirred at 37°C constant temperature water bath until dissolved to form a gelatin solution with a mass concentration of 8%, the two solutions are mixed at a volume ratio of 1:1, stirred at a rate of 200 r / min for 20 minutes in a 37°C environment to ensure uniform mixing, then a polytetrafluoroethylene spherical mold (diameter 5 cm) is used to prepare a spherical carrier matrix, 5 mL of mixed solution is injected into each mold, cooled to room temperature and demolded to obtain a hollow spherical carrier matrix, the ratio allocation unit is injected into the internal cavity of the spherical carrier matrix through a syringe, the injection amount of each carrier matrix is 2 mL, and the injection port is sealed with hot melt adhesive after injection to obtain a loaded carrier. The combination of chitosan with a molecular weight of 80000 Da and gelatin with a concentration of 8% can maintain the structural stability of the carrier in the water body for more than 30 days, while having good biocompatibility and not inhibiting microbial activity, avoiding damage to the carrier during transportation or release, which leads to premature leakage of microorganisms.

[0039] The surface of the loading carrier is layered, the outer layer is coated with a polyvinyl alcohol solution with a mass concentration of 5% by immersion method, the coating thickness is controlled to be 0.5 mm, the immersion time is 10 seconds, and then dried at 30°C for 2 hours, the polyvinyl alcohol layer can be quickly dissolved in water through water molecule penetration within 24 hours, and is used for rapid release of competitive algae; the middle layer is coated with a mixed coating solution of sodium alginate solution with a mass concentration of 10% and chitosan solution with a mass concentration of 0.5% at a volume ratio of 3:1, also by immersion method, the coating thickness is 1 mm, the immersion time is 20 seconds, and the drying is carried out at 30°C for 4 hours, the layer is gradually degraded by the exchange reaction of sodium alginate with calcium ions in the water body, and the degradation rate is controlled to be 10%±2% per day, which is used for slow release of algicidal bacteria; the inner layer is coated with a gelatin solution with a mass concentration of 15%, the coating thickness is 0.5 mm, the immersion time is 15 seconds, and the drying is carried out at 37°C for 3 hours, the gelatin needs to be slowly degraded by the decomposition of microorganisms in the water body, and the degradation rate is controlled to be 2%±0.5% per day, which is used for long-term protection of the root system of submerged plants to avoid damage before planting, and a multi-layer structure carrier is obtained after treatment.

[0040] According to the dissolution time sequence requirements of the multi-layer structure carrier, the degradation rate is accurately controlled by adjusting the concentration of each layer of material and the coating thickness, the rapid dissolution of the outer layer can make the competitive algae be released within 24 hours after being put into the water, quickly occupy the nitrogen and phosphorus nutrients in the water, and inhibit the initial growth of harmful algae; the slow degradation of the middle layer releases the algicidal bacteria at a rate of 10% per day, ensures that the concentration of algicidal bacteria in the water body is maintained at 5×10 5 CFU / mL, and continuously plays the role of algicidal bacteria; the slow degradation of the inner layer provides protection for the root system of submerged plants for at least 25 days, until the submerged plants are successfully planted in the water body and independently absorb nutrients, and finally a layered release carrier is obtained. The carrier is designed to be released in sequence, which avoids the problem of concentrated effect caused by one-time release of microorganisms and insufficient concentration in the later period, and solves the problem of short duration of the effect of microorganisms and waste of resources in the prior art.

[0041] In a specific embodiment, the process of performing step S103 can specifically include the following steps: (1) Constructing a system state vector based on the concentration of harmful algae, the concentration of algicidal bacteria, and the concentration of competitive algae, dynamically analyzing and processing the system state vector to obtain a state change rule; (2) Constructing a control input vector according to the number of layered release carriers, the interval time of release, and the depth of release, establishing a control relationship between the control input vector and the state change rule to obtain a control strategy; (3) Inputting the control strategy into an adaptive control algorithm for parameter optimization calculation, solving the control parameters through adaptive parameter adjustment and basis function calculation, and obtaining adaptive control parameters; (4) Based on the adaptive control parameters, the release quantity, release interval time and release depth in the control input vector are optimized to calculate the optimal values of each parameter, and the optimal parameter combination is obtained.

[0042] Specifically, the harmful algae concentration (unit: mg / L), the algicidal bacteria concentration (unit: CFU / mL), and the competitive algae concentration (unit: mg / L) are used to construct the system state vector, and the harmful algae concentration, the algicidal bacteria concentration, and the competitive algae concentration data of the target water area are collected every 2 hours by the water quality monitoring sensor for dynamic analysis and processing of the system state vector. The concentration data vector is arranged in chronological order to obtain the time series state data, and the change trend and rate of each concentration parameter are calculated based on the time series state data. The change rate is calculated by difference, for example, when the harmful algae concentration changes from 15 mg / L at t0 to 12 mg / L at t2, the change rate is negative, indicating that the harmful algae concentration is decreasing; when the algicidal bacteria concentration changes from 5 x 10 5 CFU / mL at t0 to 8 x 10 5 CFU / mL at t2, the change rate is positive, indicating that the algicidal bacteria concentration is increasing. The concentration change characteristics are obtained through trend analysis and processing, such as fitting the linear regression equation of the harmful algae concentration and the algicidal bacteria concentration, which provide data support for subsequent control strategies. The concentration change characteristics are input into the dynamic analysis module to determine the system state evolution path by comparing with the concentration change pattern under similar water quality conditions in the historical database and predicting the concentration change in the next 24 hours using the ARIMA model. In the prediction process, if the concentration is negative (such as the predicted harmful algae concentration is negative), it needs to be corrected to 0 to obtain the state change rule.

[0043] The release quantity, release interval time, and release depth of the layered release carrier are used to construct the control input vector, with the release quantity in units of pieces, the release interval time in units of hours, and the release depth in units of meters. The three values are combined and arranged in the order of "release quantity-release interval time-release depth" to form the control input vector. Based on the evolution trend of each concentration parameter in the state change rule, the correlation between the control input vector and the state change rule is established: when the state change rule shows that the harmful algae concentration is rising and exceeds the preset threshold (such as 10 mg / L), the influence of increasing the release quantity, shortening the release interval time, and adjusting the release depth (such as adjusting to the middle layer of the algal bloom) on the change trend of the harmful algae concentration is analyzed to determine the control direction of "increasing the release quantity, shortening the release interval time, and adjusting the release depth to 5 m depth"; when the state change rule shows that the algicidal bacteria concentration is decreasing and below the effective action threshold (such as 5 x 10 5When the concentration of algaecides was measured (CFU / mL), the effects of increasing the dosage and shortening the dosage interval on the concentration of algaecides were analyzed to determine the control direction of "increasing the dosage, maintaining the dosage depth, and shortening the dosage interval to 12 hours". By matching multiple sets of different state change scenarios with the control input adjustment direction, a control strategy was formed. This strategy solves the problem of insufficient dosage accuracy and avoids the resource waste and uneven treatment caused by traditional extensive dosage.

[0044] The control strategy is input into the adaptive control algorithm, which initializes the adaptive parameters, including the learning rate. η (Values ​​range from 0.01 to 0.05), weighting coefficient ω (initial value set to 0.5), and radial basis functions are selected as basis functions, with the following expression: In the formula x For input variables (i.e., state parameters in the control strategy). c The basis function centers are selected from historical state data using k-means clustering. σ The width of the basis function is set to half the distance between cluster centers. State parameters from the control strategy (such as harmful algae concentration, rate of change of harmful algae concentration, algaecide concentration, and rate of change of algaecide concentration) are input into the basis function for mapping, resulting in eigenvectors in the high-dimensional feature space. Subsequently, the error function is calculated by subtracting the predicted state value calculated from the current control parameters from the expected state value (e.g., the expected harmful algae concentration to be controlled within 5 mg / L). Adaptive parameters are adjusted according to the error function, with the weighting coefficient update formula as follows: ,in These represent the weights updated at step (n+1) and step n, respectively, and the learning rate. η Adjust dynamically based on error convergence (when the error is greater than 0.1). η When the value is 0.05, the error is less than 0.01. η Taking a value of 0.01, the above process is iterated repeatedly until the absolute value of the error function is less than 0.01. The adaptive parameters obtained at this point (including the final weight coefficient ω, the basis function center c, and the basis function width σ) are the adaptive control parameters. This process solves the problem of insufficient dynamic adjustment capability by autonomously optimizing the parameters through an algorithm, and avoids the limitation of traditional fixed parameter control in adapting to dynamic changes in water quality.

[0045] See Figure 2 The optimization process of the input vector is described. Based on adaptive control parameters, the number of deployments N, the deployment interval T, and the deployment depth D in the control input vector are optimized. The adaptive parameters are substituted into a preset objective function, the expression of which is: In the formula α, β, γThe weight coefficient (set according to the management priority, α = 0.4, β = 0.3, and γ = 0.3), C1 is the harmful algae concentration value, Cost(N) is the cost function of releasing N carriers, E(N, T, D) is the management effect function, which is calculated by weighting the algae-lysing bacteria concentration compliance rate and the competitive algae survival rate, and the constraint conditions are harmful algae concentration value ≤ 5 mg / L, algae-lysing bacteria concentration ≥ 5 × 10 5 CFU / mL, competitive algae concentration ≥ 0.5 mg / L, N ≥ 0, T ≥ 6 hours, and D ∈ [2m, 8m]. The gradient descent method is used to solve the optimal solution of the objective function, and the partial derivatives of the objective function with respect to N, T, and D are calculated , , , λ is the step size, and the value is 0.1, and the parameters are iteratively updated in the negative direction of the partial derivative until the objective function F(N, T, D) reaches the minimum value, at which time the corresponding N, T, and D values are the optimal parameter combination. This process realizes accurate parameter configuration through multi-objective optimization, further improves the release accuracy, and at the same time, through the cooperative constraints of harmful algae concentration, algae-lysing bacteria concentration, and competitive algae concentration in the objective function, an ecological synergistic relationship of “harmful algae inhibition-maintaining beneficial microorganisms” is constructed, solving the problem of lack of systematic ecological synergistic design.

[0046] In a specific embodiment, the system state vector is constructed based on the harmful algae concentration, the algae-lysing bacteria concentration, and the competitive algae concentration, and the state change law is obtained by dynamic analysis and processing of the system state vector, which can specifically include the following steps: (1) The harmful algae concentration value, the algae-lysing bacteria concentration value, and the competitive algae concentration value are combined and arranged to form a vector, and a concentration data vector is obtained; (2) The concentration data vector is time series sampled, and the concentration data vectors at different times are arranged in chronological order to obtain time series state data; (3) The change trend and change rate of each concentration parameter are calculated based on the time series state data, and the concentration change characteristics are obtained through data difference calculation and trend analysis processing; (4) The concentration change characteristics are input into a dynamic analysis module for law recognition, and the system state evolution path is determined through pattern matching and trend prediction calculation, and the state change law is obtained.

[0047] Specifically, to realize the construction of the system state vector based on the harmful algae concentration, the algicidal bacteria concentration, and the competitive algae concentration and obtain the state change rule, the concentration data of the three types in the target water area need to be collected first, the harmful algae concentration is in mg / L, the algicidal bacteria concentration is in CFU / mL, and the competitive algae concentration is in mg / L. The harmful algae concentration value, the algicidal bacteria concentration value, and the competitive algae concentration value obtained at the same monitoring time are combined and arranged in the order of “harmful algae concentration-algicidal bacteria concentration-competitive algae concentration” to construct a concentration data vector. For example, the harmful algae concentration is 8 mg / L, the algicidal bacteria concentration is 3×10 5 CFU / mL, and the competitive algae concentration is 0.6 mg / L at a certain time, then the corresponding concentration data vector is [8, 3×10 5 , 0.6].

[0048] After the concentration data vector is constructed, the concentration data vector is time series sampled at a fixed time interval. The sampling interval is set to 2 hours. The concentration data vector at the corresponding time is obtained each time the sampling is performed. Then, the concentration data vectors at different times are arranged in the order of sampling time to form time series state data. The change trend and change rate of each concentration parameter are calculated based on the time series state data. The change rate is calculated by data difference. For the harmful algae concentration, the harmful algae concentration values in the concentration data vectors at adjacent two times are first subtracted and then divided by the sampling interval of 2 hours to obtain the change rate. The change rates of the algicidal bacteria concentration and the competitive algae concentration are calculated in the same way. The change trend is obtained by trend analysis. The values of each concentration parameter in the time series state data are linearly regressed and fitted with the corresponding sampling time to obtain the fitting equation of the harmful algae concentration, the fitting equation of the algicidal bacteria concentration, and the fitting equation of the competitive algae concentration. The sign of the slope reflects the change direction of the concentration (positive for rising and negative for falling), and the absolute value reflects the change amplitude. The change rate and the fitting equation together constitute the concentration change characteristics.

[0049] The concentration change characteristics are input into the dynamic analysis module. The module calls the concentration change mode library under the same water type and similar pollution degree in the historical database. The current concentration change characteristics are compared with the historical modes in the mode library. The historical modes with a similarity higher than 80% are selected by mode matching. The state evolution path corresponding to the historical modes is referred to. Meanwhile, the module uses the ARIMA model to predict the trend of the change of each concentration parameter in the next 24 hours. The reference path obtained by mode matching is combined with the trend prediction result to determine the evolution path of the system state from the current time to the future 24 hours, and then the state change rule is obtained.

[0050] In a specific embodiment, the process of performing step S104 can specifically include the following steps: (1) The target water area is divided into grids according to the area of the water area, and monitoring nodes are set at the intersection of each grid to obtain a grid monitoring layout; Based on the release quantity, release interval time and release depth parameters in the optimal parameter combination, the release scheme configuration is performed on each grid unit to obtain an initial release configuration; (2) The algal bloom concentration, release cost and treatment effect of each grid unit are input into a spatial optimization objective function for weight calculation, and the release priority and resource allocation ratio of each grid are calculated by solving the objective function to obtain a spatial distribution strategy; (3) The initial release configuration is optimized and adjusted according to the spatial distribution strategy, and the final release quantity and release depth values of the layered release carrier of each grid are calculated to obtain the optimal release quantity and depth distribution of each grid.

[0051] Specifically, the geographical boundary data and area parameters of the target water area are first obtained, and the target water area is divided into grids using a square grid division method. The grid side length is set to 10 meters. If the area of the target water area is 10,000 square meters, 100 10m x 10m grid units can be divided, and each grid unit is marked as G ij (i, j represent the horizontal and vertical coordinates, respectively, and take values from 1 to 10). Monitoring nodes are set at the four corners and the center of each grid unit. Each grid corresponds to 5 monitoring nodes, and 500 monitoring nodes are set in total for 100 grids. The monitoring nodes are equipped with water quality sensors to collect algal bloom concentration, chlorophyll a concentration and other data, thereby forming a grid monitoring layout. Based on the optimal parameter combination obtained in step S103, the release quantity N0, release interval time T0 and release depth D0 are used as the basis for configuring the release scheme for each grid unit at the same proportion. For example, if N0 = 100, T0 = 24 hours and D0 = 5 meters in the optimal parameter combination, the initial release quantity of each grid unit is set to 1 (100 / 100 grids), the initial release interval time is uniformly set to 24 hours, and the initial release depth is uniformly set to 5 meters. After the release scheme configuration of all grid units is completed, an initial release configuration is obtained. This process solves the problem of uneven treatment caused by insufficient release accuracy through grid division and uniform configuration, and provides a basic framework for subsequent precise optimization.

[0052] The algal bloom concentration data C a (unit: mg / L) collected by the monitoring nodes of each grid unit is collected, the material cost, transportation cost and operation cost required for releasing the layered release carrier in each grid unit are counted, and the release cost Cost (unit: yuan) of each grid is obtained. According to historical treatment data and similar water area cases, the treatment effect I (value range 0 to 1, 1 represents complete treatment, and 0 represents no treatment effect) of each grid unit after releasing the carrier is evaluated. A spatial optimization objective function is established, and the function expression is wherein , , is a weight coefficient, set according to the management needs = 0.5 (algae bloom concentration weight), C = 0.2 (release cost weight), Cost = 0.3 (management effect weight), C amax is the maximum algae bloom concentration in all grid cells, Cost max is the maximum release cost in all grid cells. The C a , Cost, I of each grid cell are substituted into the objective function for weight calculation to obtain the objective function value of each grid cell, which is sorted in descending order to determine the release priority of each grid. According to the total release resource amount (such as the total number of carriers 100) and the proportion of the objective function value of each grid, the resource allocation proportion is calculated, for example, the objective function value of a certain grid is 0.8, and the total sum of the objective function values of all grids is 50, then the resource allocation proportion of this grid is 0.8 / 50 = 1.6%, and the corresponding allocation carrier number is 100 x 1.6% ≈ 2, thus forming a spatial distribution strategy. This function balances the algae bloom management needs and cost control through multi-factor weight calculation, avoiding the problem of resource waste in traditional release, and improving resource utilization efficiency.

[0053] According to the release priority and resource allocation proportion of each grid in the spatial distribution strategy, the initial release configuration is optimized and adjusted. For grid cells with high release priority and large resource allocation proportion, increase the number of releases, shorten the release interval time or adjust the release depth to the algae dense area, for example, the initial release number of a certain grid is 1, and according to the resource allocation proportion, it needs to be adjusted to 2, the release interval time is shortened from 24 hours to 18 hours, and the release depth is adjusted from 5 meters to 4 meters (the grid monitoring shows that the algae concentration at a depth of 4 meters is higher); for grid cells with low release priority and small resource allocation proportion, reduce the number of releases or maintain the release interval time, for example, the initial release number of a certain grid is 1, and according to the resource allocation proportion, it is adjusted to 0 (the algae concentration of this grid has fallen below the management threshold). During the adjustment process, the real-time algae concentration data collected by the monitoring nodes of each grid need to be combined to verify the rationality of the release number and depth, for example, if the algae concentration at a depth of 4 meters in a certain grid after adjustment decreases at a lower rate than expected, then the release depth is further adjusted to 3.5 meters. After adjusting all grid cells, the final stratified release carrier release number and release depth values of each grid are calculated to obtain the optimal release number and depth distribution of each grid. This optimization and adjustment process solves the problem of lack of precise spatial distribution control by dynamically matching the actual water quality conditions of the grid with the release resources, realizes differentiated management, and improves the accuracy of algae bloom management.

[0054] In a specific embodiment, the process of performing step S105 can specifically include the following steps: (1) Based on the chlorophyll a sensor, dissolved oxygen sensor, pH sensor of each monitoring node, data collection is performed, and the collected chlorophyll a concentration, dissolved oxygen concentration, and pH value data are processed to obtain real-time water quality monitoring data; (2) The real-time water quality monitoring data is subtracted from the preset expected water quality index value, and the deviation of each water quality parameter is obtained by numerical subtraction operation to obtain tracking error data; (3) The error change rate and cumulative error are calculated based on the tracking error data, and the error data is input into the adaptive parameter updating algorithm for parameter matrix adjustment calculation to obtain the updated adaptive parameter matrix; (4) The release quantity, release interval time, and release depth parameters of the layered release carrier are recalculated according to the updated adaptive parameter matrix, and the load parameters are dynamically corrected to obtain the adjusted load parameter configuration.

[0055] Specifically, based on the chlorophyll a sensor, dissolved oxygen sensor, and pH sensor deployed at each monitoring node, data collection is performed. The sampling frequency of the chlorophyll a sensor is set to 1 hour / time, and the collection range is 0-50 μg / L, which is used to obtain the chlorophyll a concentration data (unit: μg / L) of each grid unit, which indirectly reflects the algal biomass. The sampling frequency of the dissolved oxygen sensor is 1 hour / time, and the collection range is 0-20 mg / L, which is used to obtain the dissolved oxygen concentration data (unit: mg / L) for determining whether the aerobic environment of the water body is suitable for microbial survival. The sampling frequency of the pH sensor is 1 hour / time, and the collection range is 4-10 pH, which is used to monitor the influence of the acid-base degree of the water body on the activity of microorganisms. The chlorophyll a concentration data, dissolved oxygen concentration data, and pH value data collected at the same time by the same monitoring node are associated in the order of "chlorophyll a concentration data-dissolved oxygen concentration data-pH value data", and all monitoring node data are summarized to form real-time water quality monitoring data containing grid number, collection time, and three water quality parameters, such as grid G 11 The real-time water quality monitoring data at t1 is [G 11 , t1, 25 μg / L, 8 mg / L, 7.5]. This data collection and summarization process solves the problem of imperfect monitoring feedback mechanism that cannot master the treatment effect in real time, and provides data support for subsequent error calculation.

[0056] The preset expected water quality index value, wherein the expected threshold value of chlorophyll a concentration is 10 μg / L (corresponding to the level at which algal blooms are effectively controlled), the expected threshold value of dissolved oxygen concentration is 6 mg / L (satisfying the survival of microorganisms and the ecological needs of the water body), and the expected threshold value of pH value is 7.0-8.0 (taking the intermediate value 7.5 as the calculation benchmark). The difference between each parameter in the real-time water quality monitoring data and the corresponding expected threshold value is calculated, and the tracking error data includes the chlorophyll a concentration error e_Ch, the dissolved oxygen concentration error e_CO, and the pH value error e_pH. For example, the real-time chlorophyll a concentration data of a certain monitoring node is 25 μg / L, the dissolved oxygen concentration data is 8 mg / L, and the pH value data is 7.5, then the corresponding e_Ch = 25-10 = 15 μg / L, e_CO = 8-6 = 2 mg / L, and e_pH = 7.5-7.5 = 0. Through the difference operation, the actual situation of the water quality is quantitatively compared with the expected target, providing a clear error basis for subsequent parameter adjustment, and avoiding the problem of lack of dynamic adjustment basis in traditional governance.

[0057] Based on the tracking error data, the error change rate and the cumulative error are calculated. The error change rate is obtained by dividing the error difference of the adjacent two times by the time interval (1 hour), such as the chlorophyll a concentration error change rate r_Ch = (e_Ch(t)-e_Ch(t-1)) / 1, and the dissolved oxygen concentration error change rate and the pH value error change rate can also be calculated in the same way. The cumulative error is calculated by integrating the absolute value of the error in a certain time period, such as the chlorophyll a concentration cumulative error of the last 12 hours at time t , and the cumulative errors of dissolved oxygen concentration and pH value are calculated in the same way. The chlorophyll a concentration error, dissolved oxygen concentration error, pH value error, chlorophyll a concentration error change rate, dissolved oxygen concentration error change rate, pH value error change rate, and each cumulative error are input into the adaptive parameter updating algorithm. The initial parameter matrix W of the algorithm includes the adjustment coefficient w_N of the number of releases, the adjustment coefficient w_T of the interval time of releases, and the adjustment coefficient w_D of the depth of releases, and the initial value W = [w_N0, w_T0, w_D0]. According to the correlation between the error data and the parameter matrix, the parameter matrix is updated by the gradient descent method, and the update formula is W(t+1) = W(t)-ℇ×∇H(W(t)), where ℇ is the learning rate (taking the value 0.01), ∇H(W(t)) is the gradient of the target error function with respect to the parameter matrix, and the target error function H(W) = 0.4×|e_Ch|+0.3×|e_CO|+0.3×|e_pH|. Through iterative calculation, H(W) is minimized, and the updated adaptive parameter matrix is finally obtained as . This process captures the water quality change trend through the error change rate and the cumulative error, and dynamically updates the parameter matrix through the algorithm, solving the problem of insufficient dynamic adjustment capability, and making the parameter adjustment more in line with the water quality change law.

[0058] According to the updated adaptive parameter matrix , the release quantity, release interval time and release depth parameters of the layered release carrier are recalculated. The release quantity adjustment formula is , wherein is the initial release quantity 100 of each grid unit, is the chlorophyll a concentration error, is the expected threshold value of chlorophyll a concentration 10 μg / L, and if =0.02, then =100×(1-0.02×15 / 10)=97; the release interval time adjustment formula is , wherein is the initial release interval time 24 hours of each grid unit, is the dissolved oxygen concentration error, is the expected threshold value of dissolved oxygen concentration 6 mg / L, and if =0.05, then =24×(1+0.05×2 / 6)≈24.4 hours; the release depth adjustment formula is (the depth adjustment range is not more than 2 meters), wherein is the initial release depth 5 meters of each grid unit, is the chlorophyll a concentration error, is the expected threshold value of chlorophyll a concentration 15 μg / L, and if =0.5, then =5-0.5×15 / 10×2=5-1.5=3.5 meters. Through the above formula, the initial parameters are dynamically corrected to obtain the adjusted load parameter configuration , , . The configuration is dynamically optimized according to the real-time water quality error, ensures that the release of the layered release carrier always adapts to the current water quality condition, further improves the treatment accuracy, and makes up for the defects that the traditional fixed parameter release cannot adapt to environmental changes.

[0059] The artificial reef microbial load optimization method in the embodiments of the present application is described above, and the artificial reef microbial load optimization system 300 in the embodiments of the present application is described below. Please refer to Figure 3 , an embodiment of the artificial reef microbial load optimization system 300 in the embodiments of the present application includes: a symbiont construction module 301 for placing the submerged plant seedlings, the algicidal bacteria liquid and the competitive algae liquid into the sodium alginate gel matrix together, and forming a ternary symbiont unit through a calcium ion cross-linking reaction; The carrier preparation module 302 is used for loading the ternary symbiotic unit into the chitosan-gel spherical carrier in a volume ratio of 1:2:1 to prepare a layered release carrier with fast dissolution of the outer layer, slow release of the middle layer, and long-acting of the inner layer. The optimal parameter calculation module 303 is used for establishing a system state vector dynamic equation containing the concentrations of harmful algae, algicidal bacteria, and competitive algae, and calculating the optimal parameter combination of the layered release carrier release quantity, interval, and depth through an adaptive control algorithm. The release scheme optimization module 304 is used for dividing the target water area into grids and setting monitoring nodes, determining the release scheme of each grid according to the optimal parameters, and building a space optimization objective function to obtain the optimal release quantity and depth distribution of each grid. The parameter adjustment module 305 is used for collecting chlorophyll a, dissolved oxygen, and pH data of the monitoring nodes, calculating the expected and actual water quality tracking error, updating the adaptive parameter matrix according to the error, and dynamically adjusting the load parameters of the layered release carrier.

[0060] Through the cooperation of the above components, the system builds a "biological synergy-carrier controlled release-intelligent decision-making-dynamic feedback" full-chain artificial reef microbial load optimization system, realizes the closed-loop management process from microbial symbiotic body construction to carrier accurate release, and then to dynamic adjustment of water quality. The symbiotic body construction module 301 and the carrier preparation module 302 cooperate to form the "basic unit" of ecological management. Among them, the symbiotic body construction module 301 encapsulates the submerged plants, algicidal bacteria, and competitive algae into ternary symbiotic units through calcium ion cross-linking reaction, and solves the problem of low survival rate of traditional microbial release by means of the nutrient cycle of submerged plant root exudates and microorganisms. The carrier preparation module 302 loads the ternary symbiotic unit into the layered spherical carrier according to the volume ratio of 1:2:1, realizes the "on-demand release" of microorganisms through the time sequence design of fast dissolution of the outer layer (release of competitive algae), slow release of the middle layer (release of algicidal bacteria), and long-acting protection of the inner layer (protection of submerged plants), avoids the waste of resources caused by one-time release, and provides a stable carrier for ecological synergy. The two form a management core unit with high survival rate and long-acting effect.

[0061] The optimal parameter calculation module 303 and the delivery scheme optimization module 304 are linked to form a precise delivery 'decision center', wherein the optimal parameter calculation module 303 takes the concentrations of harmful algae, algicidal bacteria and competitive algae as the core to construct a system state vector, solves the optimal combination of delivery quantity, interval and depth through an adaptive control algorithm, and breaks through the limitation of traditional fixed parameters that cannot adapt to dynamic changes in water quality; the delivery scheme optimization module 304 grids the target water area and sets monitoring nodes, determines the differentiated delivery strategy of each grid in combination with the optimal parameters and a spatial optimization objective function (comprehensive algal bloom concentration, delivery cost and treatment effect), realizes the change from 'extensive delivery' to 'precise drip irrigation', and the cooperation of the two ensures efficient allocation of treatment resources and improves the spatial precision of algal bloom treatment.

[0062] The parameter adjustment module 305 forms a closed loop with all the preceding modules to construct a dynamic adaptive 'feedback mechanism', wherein the parameter adjustment module 305 collects water quality data such as chlorophyll a, dissolved oxygen and pH through monitoring nodes in real time, calculates the tracking error of actual values and expected indicators, and then updates the adaptive parameter matrix to correct the load parameters (delivery quantity, interval and depth) of the hierarchical release carrier in reverse; on the one hand, this module receives the decision results of the optimal parameter calculation module, and on the other hand, it converts water quality feedback data into parameter adjustment basis, forms a 'construction-delivery-monitoring-adjustment' closed loop with the symbiont construction, carrier preparation and delivery optimization modules, solves the problem of lack of dynamic adaptive capacity in traditional treatment, ensures real-time optimization of treatment strategies with water quality changes, and realizes the long-term effectiveness and intelligentization of algal bloom treatment.

[0063] The above Figure 3 The artificial reef microorganism load optimization system in the embodiment of the application is described in detail from the perspective of modular functional entities, and the artificial reef microorganism load optimization equipment in the embodiment of the application is described in detail from the perspective of hardware processing.

[0064] Referring to Figure 4 In the embodiment of the application, an artificial reef microorganism load optimization equipment 400 is also provided, which can be a server, and its internal structure can be as shown in Figure 4As shown. The artificial reef microbial load optimization equipment includes a processor 402, a memory 403, a display screen 404, an input device 405, a network interface 406 and a database 407 connected through a system bus 401. Among them, the processor 402 of the computer design is used to provide computing and control ability. The memory 403 of the artificial reef microbial load optimization equipment includes a non-volatile storage medium 4031 and an internal memory 4032. The non-volatile storage medium 4031 stores an operating system and a computer program. The internal memory 4032 provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database 407 of the artificial reef microbial load optimization equipment is used to store the corresponding data in this embodiment. The network interface 406 of the artificial reef microbial load optimization equipment is used to communicate with the external terminal through the network connection. The computer program is executed by the processor to realize the above-mentioned method.

[0065] Those skilled in the art can understand that, Figure 4 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the artificial reef microbial load optimization equipment to which the scheme of the present application is applied.

[0066] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium, and can also be a volatile computer readable storage medium, and the computer readable storage medium stores instructions, and when the instructions run on the computer, the computer executes the steps of the artificial reef microbial load optimization method.

[0067] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, system and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

[0068] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing an artificial reef microbial load optimization device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0069] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing a load of microorganisms in an artificial reef, characterized by, The method comprises: Step S101, submerging plant seedlings and algae-lysing bacteria and competitive algae in sodium alginate gel matrix to form a ternary symbiotic unit through calcium ion cross-linking reaction; Step S102, loading the ternary symbiotic unit into a spherical carrier constructed by chitosan-gelatin composite material at a volume ratio of 1:2:1 to prepare a layered release carrier with an outer layer of rapid dissolution, a middle layer of slow release, and an inner layer of long-acting; Step S103, establishing a dynamic equation of system state vector including harmful algae concentration, algae-lysing bacteria concentration, and competitive algae concentration, and calculating the optimal parameter combination of the release amount, release interval time, and release depth of the layered release carrier in the control input vector through an adaptive control algorithm; Step S104, dividing the target water area into grid units and setting monitoring nodes, determining the release scheme of the layered release carrier in each grid according to the optimal parameter combination, and establishing a spatial optimization objective function to solve the optimal release amount and depth distribution of each grid; Step S105, collecting chlorophyll a concentration, dissolved oxygen concentration, and pH value data of each monitoring node, calculating the tracking error of expected water quality indicators and actual monitoring values, and updating the adaptive parameter matrix according to the tracking error to realize dynamic adjustment of the load parameters of the layered release carrier.

2. The artificial reef microbial load optimization method of claim 1, wherein, The step S101 comprises: immersing the root system of the submergent plant seedlings in a composite culture medium containing algae-lysing bacteria and competitive algae to form a plant root carrier through soaking treatment; adding sodium alginate gel matrix to the composite culture medium and placing the plant root carrier in the sodium alginate gel matrix for calcium ion cross-linking reaction to obtain a solidified embedding structure; based on the circulation mechanism of submergent plant root system secreting organic acids and amino acids to provide nutrients for algae-lysing bacteria, establishing a synergistic relationship among submergent plants, algae-lysing bacteria, and competitive algae, and obtaining a synergistic ecological body; verifying the stability of the synergistic ecological body, measuring the proliferation state of algae-lysing bacteria and the activity state of competitive algae, and obtaining the ternary symbiotic unit when both algae-lysing bacteria and competitive algae reach stable activity state.

3. The artificial reef microbial loading optimization method according to claim 1, characterized by, The step S102 comprises: allocating the ternary symbiotic unit according to the volume ratio of submergent plants, algae-lysing bacteria, and competitive algae to obtain a ratio allocation unit; constructing a spherical carrier matrix based on chitosan molecular weight and gelatin concentration, and loading the ratio allocation unit into the spherical carrier matrix to obtain a loaded carrier; designing a three-layer structure of outer layer for releasing competitive algae, middle layer for releasing algae-lysing bacteria, and inner layer for protecting submergent plants on the surface of the loaded carrier to obtain a multi-layer structure carrier; controlling the degradation rate of each layer according to the dissolution time sequence of the multi-layer structure carrier, with the outer layer completely dissolving in a short time, the middle layer gradually degrading, and the inner layer slowly degrading to obtain the layered release carrier.

4. The artificial reef microbial loading optimization method according to claim 1, characterized by, The step S103 comprises: constructing a system state vector based on harmful algae concentration, algae-lysing bacteria concentration, and competitive algae concentration, and dynamically analyzing and processing the system state vector to obtain the state change law; According to the layered release carrier delivery quantity, delivery interval time, delivery depth, a control input vector is constructed, the control input vector is associated with the state change rule to establish a control relationship, and a control strategy is obtained; The control strategy is input into an adaptive control algorithm for parameter optimization calculation, and the control parameters are solved through adaptive parameter adjustment and basis function calculation, to obtain adaptive control parameters; Based on the adaptive control parameters, the delivery quantity, delivery interval time and delivery depth in the control input vector are optimized and calculated to solve the optimal values of each parameter, and the optimal parameter combination is obtained.

5. The artificial reef microbial load optimization method of claim 4, wherein, The system state vector is constructed based on the harmful algae concentration, algae-lysing bacteria concentration and competitive algae concentration, and the state change rule is obtained through dynamic analysis and processing of the system state vector, including: The concentration data vector is obtained by combining and arranging the harmful algae concentration value, algae-lysing bacteria concentration value and competitive algae concentration value; The time series sampling of the concentration data vector is performed, and the concentration data vectors at different times are arranged in chronological order to obtain time sequence state data; Based on the time sequence state data, the change trend and rate of each concentration parameter are calculated through data difference calculation and trend analysis processing to obtain concentration change characteristics; The concentration change characteristics are input into a dynamic analysis module for rule recognition, and the system state evolution path is determined through pattern matching and trend prediction calculation to obtain the state change rule.

6. The artificial reef microbial loading optimization method of claim 1, wherein, The step S104 includes: The target water area is grid-divided according to the water area, and monitoring nodes are set at the intersection points of each grid to obtain a grid monitoring layout; Based on the delivery quantity, delivery interval time and delivery depth parameters in the optimal parameter combination, a delivery scheme is configured for each grid unit to obtain an initial delivery configuration; The algal bloom concentration, delivery cost and treatment effect of each grid unit are input into a spatial optimization objective function for weight calculation, and the delivery priority and resource allocation ratio of each grid are calculated through objective function solving to obtain a spatial distribution strategy; According to the spatial distribution strategy, the initial delivery configuration is optimized and adjusted, and the final delivery quantity and delivery depth values of the layered release carrier in each grid are calculated to obtain the optimal delivery quantity and depth distribution of each grid.

7. The artificial reef microbial loading optimization method of claim 1, wherein, The step S105 includes: Based on the chlorophyll a sensor, dissolved oxygen sensor and pH sensor of each monitoring node, data is collected, and the collected chlorophyll a concentration, dissolved oxygen concentration and pH value data are processed to obtain real-time water quality monitoring data; The real-time water quality monitoring data and the preset expected water quality index value are calculated by difference, and the deviation of each water quality parameter is obtained through numerical subtraction to obtain tracking error data; Based on the tracking error data, the error change rate and cumulative error are calculated, the error data is input into an adaptive parameter update algorithm for parameter matrix adjustment calculation, and an updated adaptive parameter matrix is obtained; According to the updated adaptive parameter matrix, the delivery quantity, delivery interval time and delivery depth parameters of the layered release carrier are recalculated, and the load parameters are dynamically corrected to obtain the adjusted load parameter configuration.

8. An artificial reef microbial load optimization system, comprising: A method for optimizing a microbial load of an artificial reef according to any one of claims 1 to 7, the system for optimizing a microbial load of an artificial reef comprising: a symbiotic body construction module for placing a submerged plant seedling, an algicidal bacteria solution, and a competitive algae solution into a sodium alginate gel matrix to form a ternary symbiotic body unit through a calcium ion cross-linking reaction; a carrier preparation module for loading the ternary symbiotic body unit into a chitosan-gel spherical carrier at a volume ratio of 1:2:1 to prepare a layered release carrier with a fast-dissolving outer layer, a slow-release middle layer, and a long-acting inner layer; an optimal parameter calculation module for establishing a system state vector dynamic equation containing concentrations of harmful algae, algicidal bacteria, and competitive algae, and calculating an optimal parameter combination of the layered release carrier release amount, interval, and depth through an adaptive control algorithm; a release scheme optimization module for dividing a target water area into grids and setting monitoring nodes, determining a release scheme for each grid according to the optimal parameters, and building a spatial optimization objective function to determine the optimal release amount and depth distribution for each grid; a parameter adjustment module for collecting chlorophyll a, dissolved oxygen, and pH data from the monitoring nodes, calculating expected and actual water quality tracking errors, updating an adaptive parameter matrix according to the errors, and dynamically adjusting the layered release carrier load parameters.

9. An artificial reef microbial load optimization apparatus, comprising: A computer program product comprising a memory and a processor, the memory storing a computer program executable on the processor, the processor implementing the method for optimizing a microbial load of an artificial reef according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program product, when executed by the processor, causes the processor to implement the method for optimizing a microbial load of an artificial reef according to any one of claims 1 to 7.

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