Intelligent integrated targeted plant powder adsorbent water purification system and method

Abstract

The invention discloses an intelligent integrated targeted plant powder adsorbent water purification system and method, and provides a water pollution treatment system and method which are exclusively customized, green in restoration, efficient and economical by constructing a multi-unit collaborative intelligent treatment system. The plant powder adsorbent water purification system provides a complete and efficient scheme design flow for water pollution treatment. Aiming at water areas with different pollution conditions, the system can be used for preparing an optimal plant powder adsorbent formula, and a matched carrying device capable of improving the adsorption efficiency and realizing maximization of economic benefits can be optimally designed. In addition, the water pollution treatment scheme provided by the system is dynamic, and the optimal treatment scheme of the next stage can be recommended according to the change of the treatment progress. According to the method, aiming at heterogeneity of water area pollution characteristics, whole-process design optimization is carried out in the aspects of scheme design, plant powder adsorbent proportion optimization, treatment equipment optimization, purification process full supervision and dynamic adjustment and the like, the cost and difficulty of water body pollution treatment are reduced, and the treatment efficiency is improved.

Description

Technical Field

[0001] The present invention relates to an intelligent integrated targeted plant powder adsorbent water purification system and method, belonging to the technical field of water pollution control. Background Art

[0002] Water pollution has become a serious ecological environment problem faced by our country at present. More than half of the river sections in the seven major river systems across the country are polluted. The Liaohe River, the Haihe River, the Huaihe River, etc. are polluted more seriously. The surface water that meets the Class I and II standards only accounts for 32.2%, the Class III standards account for 28.9%, and the Class IV and V standards account for 38.9%. The pollution sources of many river basins in our country show the characteristics of diversification. In the modern industrial system, industrial sectors such as chemical engineering, printing and dyeing, and papermaking will all cause water pollution. Especially in industries such as chemical engineering and printing and dyeing, the production process is complex, involving many chemical reactions, and the wastewater generated often contains heavy metals such as mercury, cadmium, and lead, as well as a large number of refractory organic substances. At the same time, with the acceleration of the urbanization process, the continuous increase in the discharge of domestic sewage has been caused. Domestic sewage contains a large amount of organic substances, as well as nutrients such as nitrogen and phosphorus, which are the main inducements for water eutrophication. Agricultural production is also one of the important sources of water pollution, and its pollution has the characteristics of wide area, dispersion, and difficulty in control. In order to increase the yield of crops, farmers often overuse chemical fertilizers, and only a part of them is absorbed and utilized by the crops, and most of the remaining part enters the water body through surface runoff, soil infiltration, etc. These nutrients accumulate in the water body, leading to water eutrophication and triggering the "water bloom" phenomenon, which destroys the ecological balance of the water body.

[0003] At present, traditional water pollution control methods mainly include physical treatment, chemical treatment, and biological treatment methods. Physical methods are mainly based on filtration, sedimentation, centrifugal separation and other methods. Although the treatment is simple and easy, most methods can only filter out large impurities in the water body and cannot completely treat pollutants. Chemical treatment methods require chemical reactions and mass transfer to remove pollutants. The cost is extremely expensive, the steps are numerous, and due to the various chemical reactions in the treatment process, secondary pollution is very likely to occur, which is not conducive to field operations. Biological treatment methods mainly use microorganisms or living plants. Different microorganisms have different abilities to treat water pollutants. When facing different waters, it is necessary to detect the focus of water treatment and then match different microorganisms for treatment. However, the symbiotic relationship between different microorganisms is still a complex issue, and the survival of microorganisms is also extremely demanding on water bodies. Therefore, using microorganisms to treat water pollution is an extremely large and complex project. Living biological methods usually require the use of a variety of plants in combination to achieve a certain water purification effect. However, it is necessary not only to consider the symbiotic relationship between different plants, but also to strictly control the growth environment conditions of the plants. In addition, the planting, maintenance and management of living plants are large, especially in waters with complex pollution conditions, which is difficult and costly to implement. In general, traditional water pollution control methods have certain limitations due to high technical requirements, high costs, poor targeting, and easy secondary pollution.

[0004] The construction of traditional integrated sewage treatment facilities requires huge capital investment. Whether it is the construction of sewage treatment plants or the laying of sewage pipe networks, from planning and design to actual construction, and then to the later equipment maintenance and updating, each stage is accompanied by high costs. In addition, traditional integrated equipment cannot carry out targeted decontamination for complex and diverse polluted water bodies. Traditional processes rely on manual operations, and the treatment effect is easily affected by external factors, making it difficult to adapt to the needs of small-scale, decentralized scenarios. Artificial wetlands generally have the defect of occupying a large area. Under the constraints of limited land resources, improving the purification efficiency of artificial wetlands is a major pain point. Especially under the stress environment of low temperature in the north, inland and coastal salinization, the operating efficiency of artificial wetlands decreases, and the promotion and application of artificial wetlands are huge challenges.

[0005] With the progress of society, the problem of water pollution has become increasingly prominent. Traditional treatment methods can no longer meet the current needs. Therefore, the use of more advanced intelligent technologies for water pollution treatment has attracted extensive attention. Intelligent technologies have played an important role in water pollution control and have outstanding performance in improving water treatment efficiency and water quality. The present invention proposes an intelligent control system and method for in-situ water purification using plant powder adsorbents, including a sampling unit, a water sample analysis unit, an intelligent control center, an adsorbent preparation unit, an environmental analysis unit, a device manufacturing unit, and a commissioning unit. Compared with traditional methods, this system can achieve intelligent and automated high-efficiency green pollution removal. By inputting the sampling data and water sample analysis data, the intelligent control center can obtain the appropriate adsorbent formula according to the algorithm, and at the same time obtain the basin type of the sampling point, and can select the appropriate supporting products to create a customized pollution removal plan. In addition, the recovery of the adsorbent can be realized. It can achieve full coverage and no dead angle of water samples in the sampling unit, intelligent management of the system in the intelligent control center, real-time monitoring of water quality and environment in the water sample analysis unit and environmental analysis unit, specific, efficient, green and economical water pollution treatment in the adsorbent preparation unit and device manufacturing unit, and dynamic adjustment in the commissioning unit to achieve the closed-loop of the system.

[0006] A Chinese patent with the publication number CN 119551765 A discloses a constant temperature circulating water treatment system, which includes the following steps: constructing a sensor module to collect, preliminarily process and transmit the data of sewage treatment equipment; designing a feature fusion module to receive the data of the sensor module, select, extract and fuse the data features; developing an adaptive multi-modal feature fusion fault prediction model to automatically adjust the feature fusion weight according to the actual and historical operation data to calculate the equipment failure probability; building a real-time monitoring and warning module to analyze the real-time and fault prediction results to monitor whether the equipment has an abnormal working state, and issue a warning when abnormal.

[0007] However, this operation monitoring system for sewage treatment equipment based on artificial intelligence has high system construction and maintenance costs and high usage costs, and is more suitable for optimizing the water pollution treatment of large sewage treatment plants or complex processes. It cannot adapt to various basins in practical applications and specifically solve the specific pollution conditions of the basins. In resource-limited environments such as small sewage treatment stations, park lakes, and urban inland rivers, its popularization and application are greatly restricted.

[0008] The existing water pollution treatment technologies have the following main defects and deficiencies in practical applications:

[0009] (1) The core problems of physical treatment technologies lie in the low treatment efficiency of dissolved pollutants and the relatively high operating costs. Limited by Stokes' law, sedimentation can only effectively remove suspended particles with a density greater than that of water, and is insufficient in separating dissolved organic pollutants and low-concentration heavy metal ions; filtration and membrane separation technologies (such as microfiltration, reverse osmosis) can intercept micron-sized particles, but the membrane modules are easily blocked by colloidal substances or organic fouling, resulting in flux decay and frequent chemical cleaning, significantly increasing energy consumption and maintenance costs. In addition, physical methods generally pose a risk of secondary pollution. For example, if the adsorption-saturated activated carbon is not regenerated in time, it will release the adsorbed substances to form new pollution sources, and the treatment of reverse osmosis concentrate still requires additional process support.

[0010] (2) Chemical treatment technologies are prone to secondary environmental risks during the pollutant degradation process. Strong oxidation processes (such as ozone, Fenton's reagent) may generate more toxic intermediate products through free radical chain reactions. Typically, the carcinogenic halogenated hydrocarbons produced by chlorine disinfection; the metal salt flocculants (aluminum salts / iron salts) used in chemical precipitation methods will cause fluctuations in the pH value of the water body and metal ion residues, and long-term accumulation may damage the balance of the aquatic ecosystem. More critically, chemical methods require precise control of reaction kinetic parameters (pH value, redox potential, chemical dosage ratio). For industrial wastewater containing multi-component pollutants, the complexity of chemical agent compatibility increases exponentially, resulting in a non-linear growth relationship between treatment costs and pollutant concentrations.

[0011] (3) The limitations of biological treatment technologies stem from the environmental sensitivity of microbial metabolic activities. The nitrification / denitrification bacterial communities have relatively low tolerance thresholds for temperature fluctuations (±5°C), dissolved oxygen concentration (<0.5 mg / L), and toxic substances (such as heavy metal ion concentration >1 ppm), and are prone to bacterial community inactivation in the presence of high salts or refractory organic compounds (such as polycyclic aromatic hydrocarbons, antibiotics). In addition, the start-up stage of the biological treatment system requires a seed acclimation period of up to several weeks, and a slight deviation in the carbon-nitrogen-phosphorus ratio (usually required to be 100:5:1) will lead to a decrease in nitrogen and phosphorus removal efficiency. The excess sludge generated during the treatment process contains extracellular polymeric substances and pathogenic microorganisms, and the cost of its dewatering, drying, and harmless disposal accounts for 30%-40% of the total operating cost of the system. If landfilled or incinerated improperly, it may cause secondary pollution.

[0012] (4) There are ecological safety risks in the engineering application of living biological adsorption systems. If the hyperaccumulator plants (such as water hyacinth and reed) selected for ecological floating islands are not genetically modified, they may form biological invasions through pollen transmission, resulting in a 30%-60% decrease in the diversity index of local aquatic plant communities. In waters with heavy metal complex pollution (such as Cd>0.1mg / L + Pb>0.5mg / L), the tolerance period of aquatic plants is usually shortened to 2-3 months. After exceeding the critical value, the organic acids secreted by the roots instead promote the desorption of heavy metals. The mineralization and decomposition of dead plants will also release nutrients such as N and P, increasing the chlorophyll a concentration in the water by 50%-100% and inducing eutrophication.

[0013] (6) The artificial wetland technology is limited by space efficiency and long-term operation and maintenance costs. The hydraulic loading rate of traditional surface flow artificial wetlands is only 0.1-0.5m 3 / (m 2 ·d). Treating 10,000 tons of water bodies requires an area of 10-15 hectares, and the application cost in urban built-up areas increases by 3-5 times. A perfect model has not been established for the quantitative relationship between the design parameters of the substrate layer (particle size distribution, porosity, permeability coefficient) and the pollutant removal rate. In actual projects, the fluctuation range of TN / TP removal rate reaches ±25%. More seriously, if the biomass produced by wetland plants every year (dry weight about 5-8kg / m 2 ) is not harvested in time, the leaching rate of bound heavy metals in the sediment will increase by 2-3 orders of magnitude during the decay process, resulting in the rebound of pollutants after the system operates for 3-5 years. Summary of the Invention

[0014] In order to overcome the shortcomings of the prior art, the present invention proposes an intelligent integrated targeted plant powder adsorbent water purification system and method. By using the plant powder adsorbent, the system of the present invention realizes the concept of ecological environmental protection on the premise of efficiently adsorbing water pollution. The plant powder adsorbent water treatment system of the present invention provides a complete and efficient solution for water pollution treatment. For waters with different pollution conditions, the system can formulate the most suitable plant powder adsorbent formula, and according to the different conditions of the water area, it is equipped with supporting devices that can improve the adsorption efficiency and maximize economic benefits. In addition, the water pollution treatment solution provided by the system is dynamic and will change the most suitable treatment plan for the next stage according to the changes in the treatment progress. This new type of plant powder adsorbent water purification system solves the problems of high difficulty and high cost in traditional water pollution treatment, and maximizes economic benefits on the basis of realizing efficient treatment.

[0015] The present invention is realized through the following technical solutions: an intelligent integrated targeted plant powder adsorbent water purification system and method, the water purification system includes a sampling unit, a water sample analysis unit, an intelligent control center, an adsorbent preparation unit, an environmental analysis unit, a device manufacturing unit, and an adsorbent recovery unit.

[0016] The sampling unit inputs the natural geographical information, ecological environment information, and human activity information obtained from on-site inspections into the intelligent control center. The intelligent control center is connected to the water sample analysis unit, adsorbent preparation unit, and environmental analysis unit. The water sample analysis unit is equipped with a full-parameter water quality detector, which transmits the measured heavy metal concentration, nutrient salt concentration, pH value, and soluble pollutant data to the intelligent control center. After analyzing the soluble pollutant data, the intelligent control center calculates the required mass of the adsorbent and transmits the data to the adsorbent preparation unit in real time. At the same time, the intelligent control center determines the watershed type through environmental analysis using GIS and satellite maps, and transmits the watershed type data to the environmental analysis unit. Meanwhile, the data of the watershed type is transmitted to the device manufacturing unit. The device manufacturing unit determines whether to use the self-circulating ecological floating boat or the detachable powder carrier device of the device manufacturing unit for water purification according to the watershed type. After the water purification is completed, the adsorbent enters the adsorbent recovery unit again.

[0017] The specific steps for the water purification system to purify water are as follows:

[0018] Step1: The sampling unit conducts full-range coverage sampling, including the natural geographical information of the sampling points: watershed area index N1, narrowest river index N2, average slope index N3, river network density index N4, ecological environment information: vegetation coverage index E1, biodiversity index E2, water quality pollution index E3, wind speed E4, and human activity information: population density index H1, land development intensity index H2, water resource utilization intensity index H3, and vessel activity intensity index H4, and inputs the data into the intelligent control center. UAVs or remote sensing technologies can be added for large-scale and high-precision auxiliary sampling. At the same time, more types of sensors, such as pH sensors and dissolved oxygen sensors, can be introduced to obtain more comprehensive water quality data;

[0019] Step2: The full-parameter water quality detector of the water sample analysis unit integrates digestion and determination. It intelligently monitors the COD, ammonia nitrogen, total phosphorus, total nitrogen content, and pH value. The heavy metal detector in the water quality detector has 360° rotating colorimetric tube detection and colorimetric cell detection to accurately monitor the content of heavy metals such as copper, cadmium, zinc, and lead in water. The water sample analysis unit inputs the measured data into the intelligent control center in real time;

[0020] Step3: The intelligent control center uses the dynamic multi-dimensional collaborative watershed classification algorithm DMS-WCS to analyze the data input in Step1 and Step2, and obtains three watershed types: natural geographical index N type, ecological environment index E type, and human activity index H type;

[0021] Step4: The intelligent control center determines the supporting carrier device for the plant powder adsorbent as a self-circulating ecological floating boat or a detachable powder carrier tank according to the above three watershed types;

[0022] Step 5: The device manufacturing unit determines the device size according to the basin type and application scenario and manufactures the device;

[0023] Step 6: The intelligent control center uses the dynamic robust collaborative adsorption optimization algorithm DRSAO to calculate the effective adsorption capacity of the input parameters with the data input in Step 1 and Step 2. Subsequently, dynamic calculation of the plant powder adsorbent and batch iteration processing are carried out to analyze the collaborative adsorption optimization of multiple metal ions and organic particles, and finally the plant powder adsorbent formula is obtained;

[0024] Step 7: The adsorbent preparation unit prepares the plant powder adsorbent according to the obtained optimal formula;

[0025] Step 8: The adsorbent preparation unit puts the prepared adsorbent into the device prepared in Step 5 for water treatment;

[0026] Step 9: The intelligent control center calculates the time for recycling the plant powder adsorbent after reaching the maximum adsorption efficiency through the dynamic economic collaborative recovery optimization algorithm DESRO, determines the maximum adsorption efficiency and recovery time of the plant powder adsorbent, and transmits the data to the adsorbent recovery unit;

[0027] Step 10: The adsorbent recovery unit regularly replaces and recovers the adsorbent in the device.

[0028] The intelligent control center adds an intelligent early warning function. When the water quality data is abnormal, it can automatically trigger the early warning mechanism and notify the relevant personnel for handling in a timely manner.

[0029] Furthermore, the calculation of Step 3 by the intelligent control center is as follows:

[0030] The intelligent control center divides the water body into three index types according to the sampling data of the sampling unit and the water sample analysis unit. First, the comprehensive water body pollution index P is calculated:

[0031]

[0032] Among them, P is the comprehensive water body pollution index, ω is the weight coefficient, and the value range is [0, 1], C k represents the measured concentration of substances after water sample analysis, and S k represents the standard concentration limit of heavy metals in Class V water. See Table 1:

[0033] Table 1 (unit: mg / L)

[0034] Nickel Copper Zinc Cadmium Chromium (hexavalent) Mercury Arsenic 0.005 1.0 1.0 0.005 0.05 0.0001 0.001 Lead Antimony Barium Cobalt Molybdenum Silver Selenium 0.01 0.0001 0.01 0.005 0.001 0.001 0.01

[0035] According to the P value, the indicators are classified as follows: if P = 0 - 4.5, the water body is a natural geographical indicator N; if P = 4.5 - 7.0, the water body is an ecological environment indicator E; if P > 7.0, the water body is a human activity indicator H. After determining the indicator classification, calculate and correct each indicator type:

[0036] ① Natural geographical indicator N type: It consists of natural geographical indicators such as basin area indicator N1, narrowest river indicator N2, average slope indicator N3, and river network density indicator N4. The calculation formula is:

[0037] Unify the units of each indicator, that is, convert each indicator to the range of [0, 1]:

[0038]

[0039] maxN i 、minN i are the maximum and minimum values of N i respectively, and the calculation formula is:

[0040]

[0041] where, a i is the weight coefficient, and considering the influence of the natural environment, introduce the natural correction coefficient α N = 0.8 - 1.2, and the corrected natural geographical indicator is: N effective = N × α N ;

[0042] If N effective ∈ [0, 3.0), then the water body is a high ecological integrity type, and it is suitable for a flexible and convenient detachable powder carrier slot (this type of device has the advantages of simple operation and high flexibility); if N effective ∈ [3.0, 4.8) is a moderately ecological stable type, and it is suitable for a flexible and convenient detachable powder carrier slot and a flexible and convenient self-circulating ecological floating boat (which can freely shuttle in narrow waters, is easy to use, and meets the needs of fast travel operations); if N effective ∈ [4.8, +∞) is a slightly ecological stress type, and a flexible and convenient self-circulating ecological floating boat;

[0043] ② Ecological environment indicator E type: It consists of ecological environment indicators such as vegetation coverage indicator E1, biodiversity indicator E2, water pollution indicator E3, and wind speed E4. Unify the units of them, that is, convert each indicator to the range of [0, 1]:

[0044]

[0045] maxE i 、minEi are respectively E i the maximum and minimum values, and the calculation formula is:

[0046]

[0047] where, a i is the weight coefficient Introduce the ecological correction coefficient α E ∈(0.7 - 1.3)

[0048] The corrected ecological environment index is: E effective = E × α E

[0049] If E effective ∈[0, 2.8), then the water body is an ecological coordination type, and is adapted to an all - in - one practical detachable powder carrier tank (with good stability, able to better resist wind and waves, and also having a certain passenger - carrying capacity); if E effective ∈[2.8, 5.2), then the water body is a balanced human activity type, and is adapted to an all - in - one practical detachable powder carrier tank and an all - in - one practical self - circulating ecological floating ship (taking into account a certain transportation capacity and being able to meet more sailing time); if E effective ∈[5.2, +∞), then the water body is a human activity adaptation type, and is adapted to an all - in - one practical self - circulating ecological floating ship;

[0050] ③ Human activity index H type:

[0051] It consists of the population density index H1, the land development intensity index H2, the water resource utilization intensity index H3, and the vessel activity intensity index H4 of the human activity index. Unify its unit, that is, each index is converted to the range of [0, 1]:

[0052]

[0053] maxH i 、minH i are respectively the maximum and minimum values of H i and the calculation formula is:

[0054]

[0055] where, a i is the weight coefficient Introduce the human activity correction coefficient α H ∈(0.9 - 1.1), and the corrected human activity index is: H effective = H × α H

[0056] If H effective∈[0, 3.2), then the water body is of the human - developed type, and it is suitable for a heavy - load long - voyage detachable powder - carrying tank (with relatively high safety performance to cope with slightly larger river basins and sufficient carrying capacity); if H effective ∈[3.2, 4.4), then the water body is of the moderately - disturbed type, and it is suitable for a heavy - load long - voyage detachable powder - carrying tank and a heavy - load long - voyage self - circulating ecological floating ship (with higher safety performance, stronger endurance, capable of operating in large river basins for a long time, and having stronger carrying capacity, capable of achieving economies of scale, etc.); if H effective ∈[4.4, +∞), then the water body is of the low - level restoration type, and it is suitable for an all - in - one practical self - circulating ecological floating ship.

[0057] Furthermore, in Step6, the intelligent control center (3) uses the dynamic robust collaborative adsorption optimization algorithm DRSAO to calculate the effective adsorption capacity of the input parameters with the data input in Step1 and Step2, specifically as follows:

[0058] ① Input parameters and effective adsorption capacity calculation

[0059] The heavy metal content and organic matter particle concentration C0 of the water sample measured by the water sample analysis unit, and the expected heavy metal content and organic matter particle concentration C of the treated water are formulated according to the national water quality discharge standard t , the volume V of the treated water body is measured, and the maximum adsorption capacity Q of different plant powder adsorbents is input max , and the actual effective adsorption capacity is calculated by combining the environmental correction factors α, β, and γ:

[0060] Among them, the environmental correction factors are:

[0061] α is the pH correction factor. The best adsorption effect of the plant powder adsorbent usually occurs in a specific pH range (such as pH = 5 - 6). When deviating, it is necessary to multiply by the correction factor α (0 - 1). When the pH deviates from the specific range by greater than or equal to 1.5, take α = 0 - 0.5; when the pH deviates from the specific range by less than 1.5, take α = 0.5 - 1;

[0062] β is the temperature correction factor. The adsorption capacity of the plant powder adsorbent is closely related to the temperature. The adsorption efficiency is the best at 25°C. High temperature or low temperature both inhibit the adsorption effect. When adsorbing in different temperature environments, it is necessary to multiply by the correction factor β (0.8 - 1). When the temperature deviates from 25°C by greater than or equal to 20°C, take β = 0.8 - 0.9; when the temperature deviates from 25°C by less than 20°C, take β = 0.9 - 1;

[0063] γ is the competitive repair inhibition coefficient. The presence of other heavy metal ions or organic particles in the water body affects the adsorption capacity of the plant powder adsorbent for specific heavy metal ions or organic particles. It is necessary to introduce the competitive inhibition coefficient γ (0-1). When there are 17 or more other heavy metal ions or organic particles in the water body, take γ = 0-0.5. When there are less than 17 other heavy metal ions or organic particles in the water body, take γ = 0.5-1;

[0064] Expand the environmental correction coefficients α, β, and γ into a robust optimization model:

[0065]

[0066] s.t.Q effective =Q max ·α·β·γ

[0067] α ∈ [α min , 1], β ∈ [β min , β max , γ ∈ γ min , 1]

[0068] Dynamic calculation of the plant powder adsorbent:

[0069] Introduce a safety factor k and calculate the mass of the plant powder:

[0070]

[0071] Among them, the safety factor k is used to ensure that the adsorbent can cope with various uncertainties and potential efficiency losses in actual adsorption. Based on engineering experience and experimental data, it aims to ensure the safety and reliability of the system. k takes 1.2-2.0. According to the degree, it is divided into mild (k = 1.2-1.4), moderate (k = 1.5-1.7), and severe (k = 1.8-2.0);

[0072] Introduce constraint conditions:

[0073]

[0074] The emission standard constraint refers to the requirement that the heavy metal content and organic particle concentration C t value in the treated water body is required to be less than or equal to the emission standard value C std ;

[0075] The maximum batch limit refers to the maximum number of experiments set to prevent excessive experimental energy consumption and save costs, which should be less than or equal to N max ;

[0076] The budget constraint refers to the requirement that the mass M of the newly prepared plant powder adsorbent should be less than or equal to the constraint value M budget, avoid raw material waste and loss problems and control costs;

[0077] ②Iterative processing in batches

[0078] The adsorption capacity of the plant powder adsorbent is affected by various influencing factors, resulting in the water body not reaching the target concentration C after a single treatment t , and then calculate the remaining concentration:

[0079]

[0080] And use C remaining as the new C0 for iteration until the standard is reached;

[0081] Supplement the transfer equation:

[0082]

[0083] The transfer equation is to add a noise term to the iterative equation for calculating the remaining concentration. Among them, the noise term aims to make the model closer to reality and is used to analyze the robustness of the system, the prediction error range, or consider random interference when designing control strategies, so as to avoid the model becoming completely deterministic and unable to reflect the random behavior of the real system;

[0084] ③Optimization of the co-adsorption of multiple metal ions and organic particles

[0085] For a water body containing multiple heavy metal ions and organic particles, calculate the mass M of the plant powder adsorbent required to adsorb each metal ion and organic particle respectively i , take the maximum value M = max(M1, M2, M3,..., M n ) to ensure that the concentrations of all heavy metal ions and organic particles meet the standards,

[0086] Introduce a coupling adsorption matrix A ∈ R m×n , which describes the adsorption efficiency of m plant powder adsorbents for n heavy metal ions and organic particles:

[0087]

[0088] Constraint:

[0089] A·M ≥ C req (Meet the constraint)

[0090] Among them, the first item (adsorption effect fitting item): C 0,j refers to the concentration of the jth pollutant at the initial moment, C t,j refers to the concentration of the jth pollutant after the treatment time t, Q effective,j refers to the adsorption efficiency of the plant powder adsorbent for the jth pollutant, C reqis the vector of target concentrations for each metal. This term represents minimizing the difference between the actual removal concentration and the theoretical adsorption efficiency. The sum-of-squares form aims to make the adsorption effect as close as possible to the expectation;

[0091] The second term (regularization term): refers to the sum of the squares of all elements of matrix A. ρ is the regularization coefficient, which is used to balance the fitting accuracy and the model complexity. This term prevents the model from overfitting the experimental data, ensures that the element values of the adsorption matrix A are not too large, and enhances the generalization ability;

[0092] Step9 The recovery of the plant powder adsorbent is specifically as follows:

[0093] The plant powder adsorbent can be recycled after reaching the maximum adsorption efficiency. By constructing a "Mixed-Integer Nonlinear Programming (MINLP)" model, the dosage of the plant powder adsorbent and the recovery time are synchronized and optimized:

[0094]

[0095] Among them, the first item, the cost of adsorbent dosage: M i represents the usage amount of the i-th adsorbent. ω1 refers to the weight coefficient of the adsorbent dosage, and its value ranges from 0.5 to 0.7, reflecting the importance of the adsorbent cost in the total objective. This term represents minimizing the total cost of the adsorbent dosage. If the cost is relatively high, ω1 should take a larger value of 0.6 to 0.7 to reduce the adsorbent dosage. If the cost is relatively low, ω1 should take a smaller value of 0.5 to 0.6;

[0096] The second item, the cost of recovery time: Q effective,i refers to the adsorption efficiency coefficient of the i-th adsorbent, v 0,i refers to the initial adsorption efficiency of the i-th adsorbent, s i The recovery time of the i-th adsorbent, which is a decision variable. ω2 is the weight coefficient of the recovery time, reflecting the importance of the recovery time cost in the total objective. This term represents minimizing the recovery time cost of all adsorbents. If the time cost is relatively high, ω2 should take a larger value of 0.2 to 0.3 to shorten the recovery time. If the time cost is relatively low, ω2 should take a smaller value of 0.1 to 0.2;

[0097] Constraint condition: M total The upper limit of the total available amount of the adsorbent. The total dosage constraint equation (②) means that the total dosage of all adsorbents cannot exceed the available resources. t max The upper limit of the single recovery time. The recovery time constraint equation (③) means that the recovery time of each adsorbent cannot exceed the allowed maximum value.

[0098] For water bodies containing multiple heavy metal ions and organic particles, calculate the time t required for the plant powder adsorbent to adsorb various metal ions and organic particles respectivelyi , the average time required to adsorb water bodies containing various heavy metal ions and organic particles is obtained:

[0099]

[0100] In the Step10 adsorbent recovery unit, the saturated plant powder adsorbent in the self-circulating ecological floating boat or the detachable powder carrier device put into the basin is subjected to humification treatment to achieve heavy metal recovery and obtain organic fertilizer.

[0101] The formula will be imported into the plant powder adsorbent preparation system in real time. The plant powder adsorbent preparation system is the system disclosed in the patent application number: 2025204041678. Through this system, an efficient, green and economical plant powder adsorbent can be customized and put into the powder carrier tank of the self-circulating ecological floating boat or the detachable powder carrier device manufactured by the device manufacturing unit; the powder carrier tank in the detachable powder carrier device is the carrier tank in the carrier device applied for with the patent application number 2025102823124. The self-circulating ecological floating boat is the ecological floating boat device disclosed in the application number 2024111928915.

[0102] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0103] (1) The system of the present invention significantly improves the pollutant removal efficiency and reduces costs. Based on the intelligent proportioning technology of dynamic water quality monitoring, the adsorption capacity of the plant powder adsorbent for dissolved heavy metals (such as Cu 2+ , Zn 2+ , Cd 2+ ) is greatly improved, and the heavy metals and organic substances in the adsorbent are reused through the over-humification recovery system. At the same time, the adsorption selectivity coefficient (K s ) is increased to 8.6 - 12.4, effectively inhibiting the competitive adsorption of Ca 2+ / Mg 2+ and other ions, and the removal rate of target pollutants is stable above 95%.

[0104] (2) The modular device design of the present invention breaks through the spatial and operation and maintenance limitations of traditional treatment. The water passing capacity of the self-circulating ecological floating boat reaches 11t / d, the device volume is concentrated and suitable for various basin conditions, and the operation and maintenance energy consumption is reduced (the proportion of solar power supply is ≥90%). The detachable powder carrier device can be attached to the existing device or carried to the river bank. By using the water power of the water area itself and combining with the GIS environment adaptation technology, the fluctuation range of the pollutant removal rate of the system in complex water areas (N / E / H type) is reduced from ±25% to ±8%.

[0105] (3) The full-process resource utilization technology of the present invention eliminates the risk of secondary pollution. The humification treatment of the adsorbent improves the heavy metal recovery rate, and the total nutrients (N + P2O2 + K2O) of the simultaneously generated organic fertilizer are ≥ 12%, meeting the agricultural standard (NY / T525-2021). The comprehensive operating cost of the system is reduced by 45% - 50% compared with the traditional method, and no chemical agents are added, avoiding the generation of toxic by-products such as halogenated hydrocarbons (detection limit < 0.001 μg / L), realizing a double closed-loop of pollutant reduction and resource regeneration.

[0106] (4) The present invention obtains the applied watershed type according to the dynamic multi-dimensional collaborative watershed classification algorithm (DMS-WCS), calculates the formula of the new plant powder adsorbent using the dynamic robust collaborative adsorption optimization algorithm (DRSAO), and determines the maximum adsorption efficiency and recovery time of the plant powder adsorbent through the dynamic economic collaborative recovery optimization algorithm (DESRO), realizing the integrated, intelligent, and efficient treatment of water pollution. Description of the Drawings

[0107] Figure 1 It is a schematic diagram of the connection structure of each unit of the present invention;

[0108] Figure 2 It is a flow chart of the water area classification calculation of the present invention;

[0109] Figure 3 It is a flow chart of the formula calculation of the plant powder adsorbent. Detailed Embodiments

[0110] The present invention will be further described in detail below with reference to the drawings and specific embodiments, but the protection scope of the present invention is not limited to the content described.

[0111] Example 1: As Figure 1 shown, the intelligent integrated targeted plant powder adsorbent water purification system and method of the present invention are characterized in that the water purification system includes a sampling unit 1, a water sample analysis unit 2, an intelligent control center 3, an adsorbent preparation unit 4, an environmental analysis unit 5, a device manufacturing unit 6, and an adsorbent recovery unit 7;

[0112] The sampling unit 1 inputs the natural geographical information, ecological environment information, and human activity information obtained from on-site inspections into the intelligent control center 3. The intelligent control center 3 is connected to the water sample analysis unit 2, the adsorbent preparation unit 4, and the environmental analysis unit 5. The water sample analysis unit 2 is equipped with a full-parameter water quality detector, which transmits the measured heavy metal concentration, nutrient salt concentration, pH value, and soluble pollutant data to the intelligent control center 3. After analyzing the soluble pollutant data, the intelligent control center 3 calculates the required mass of the adsorbent and transmits the data to the adsorbent preparation unit 4 in real time. At the same time, the intelligent control center 3 determines the basin type through environmental analysis using GIS and satellite maps, and transmits the basin type data to the environmental analysis unit 5. Meanwhile, the data of the basin type is transmitted to the device manufacturing unit 6. The device manufacturing unit 6 determines whether to use the self-circulating ecological floating boat or the detachable powder carrier device of the device manufacturing unit 6 for water purification according to the basin type. After the water purification is completed, the adsorbent enters the adsorbent recovery unit 7.

[0113] The specific steps of the water purification system are as follows:

[0114] Step1: The sampling unit 1 takes samples for full-range coverage, including the natural geographical information of the sampling points: the basin area index N1, the narrowest river index N2, the average slope index N3, and the river network density index N4, the ecological environment information: the vegetation coverage index E1, the biodiversity index E2, the water pollution index E3, and the wind speed E4, and the human activity information: the population density index H1, the land development intensity index H2, the water resource utilization intensity index H3, and the vessel activity intensity index H4, and inputs the data into the intelligent control center 3.

[0115] Step2: The full-parameter water quality detector of the water sample analysis unit 2 integrates digestion and determination. It intelligently monitors the COD, ammonia nitrogen, total phosphorus, total nitrogen content, and pH value. The heavy metal detector in the water quality detector has 360° rotating colorimetric tube detection and colorimetric cuvette detection to accurately monitor the content of heavy metals such as copper, cadmium, zinc, and lead in water. The water sample analysis unit 2 inputs the measured data into the intelligent control center 3 in real time.

[0116] Step3: The intelligent control center (3) obtains three basin types according to the data input in Step1 and Step2 using the dynamic multi-dimensional collaborative basin classification algorithm DMS-WCS: the natural geographical index N type, the ecological environment index E type, and the human activity index H type, as Figure 2 shown, and the specific calculation is as follows:

[0117] The intelligent control center classifies the water body into three index types according to the sampling data of the sampling unit and the water sample analysis unit. First, calculate the comprehensive water body pollution index P:

[0118]

[0119] Among them, P is the comprehensive water body pollution index, ω is the weight coefficient, and the value range is [0, 1], C k represents the measured concentration of substances after water sample analysis, S k represents the standard concentration limit of heavy metals in Class V water. See Table 1:

[0120] Table 1 (unit: mg / L)

[0121] Nickel Copper Zinc Cadmium Chromium (hexavalent) Mercury Arsenic 0.005 1.0 1.0 0.005 0.05 0.0001 0.001 Lead Antimony Barium Cobalt Molybdenum Silver Selenium 0.01 0.0001 0.01 0.005 0.001 0.001 0.01

[0122] According to the P value, index classification is carried out again: if P = 0 - 4.5, then the water body is the natural geographical index N; if P = 4.5 - 7.0, then the water body is the ecological environment index E, and if P > 7.0, then the water body is the human activity index H; after determining the index classification, calculations and corrections are carried out for each index type:

[0123] ① Natural geographical index N type: It consists of the basin area index N1, the narrowest river index N2, the average slope index N3, and the river network density index N4 of natural geographical indexes. The calculation formula is:

[0124] Unify the units of each index, that is, convert each index to the range of [0, 1]:

[0125]

[0126] maxN i 、minN i are the maximum and minimum values of N i respectively, and the calculation formula is:

[0127]

[0128] Among them, a i is the weight coefficient, and considering the influence of the natural environment, the natural correction coefficient α N = 0.8 - 1.2 is introduced. The corrected natural geographical index is: N effective = N × α N ;

[0129] If N effective ∈ [0, 3.0), then the water body is of high ecological integrity type; if N effective ∈ [3.0, 4.8) is of moderate ecological stability type; if N effective ∈ [4.8, +∞) is of mild ecological stress type;

[0130] ② Ecological environment index E type: It consists of the vegetation coverage index E1, the biodiversity index E2, the water quality pollution index E3, and the wind speed E4 of ecological environment indexes. Unify the units of them, that is, convert each index to the range of [0, 1]:

[0131]

[0132] maxE i 、minE i are the maximum and minimum values of E i respectively, and the calculation formula is:

[0133]

[0134] where, a i is the weight coefficient Introduce the ecological correction coefficient α E ∈(0.7 - 1.3)

[0135] The corrected ecological environment index is: E effective = E × α E

[0136] If E effective ∈[0, 2.8), then the water body is an ecological coordination type; if E effective ∈[2.8, 5.2), then the water body is a human activity balance type; if E effective ∈[5.2, +∞), then the water body is a human activity adaptation type;

[0137] ③ Human activity index H type:

[0138] It consists of the human activity index composed of the population density index H1, the land development intensity index H2, the water resource utilization intensity index H3, and the vessel activity intensity index H4. Unify their units, that is, each index is converted to the range of [0, 1]:

[0139]

[0140] maxH i 、minH i are the maximum and minimum values of H i respectively, and the calculation formula is:

[0141]

[0142] where, a i is the weight coefficient Introduce the human activity correction coefficient α H ∈(0.9 - 1.1), and the corrected human activity index is: H effective = H × α H

[0143] If H effective ∈[0, 3.2), then the water body is a human development type; if H effective∈[3.2, 4.4), then the water body is moderately disturbed; if H effective ∈[4.4, +∞), then the water body is of low-level restoration type;

[0144] Sampling points are taken from a certain tailings and its water samples are analyzed to obtain the concentrations of various metal ions (the concentration of Cu 2+ is 1.0 mg / L, the concentration of Zn 2+ is 2.0 mg / L, the concentration of Cr 6+ ion is 0.1 mg / L, the concentration of Ni 2+ ion is 0.2 mg / L, etc.). Through calculation, P = 4.72 is obtained, and the water area type is classified as the ecological environment index type (E type). Data analysis is carried out on the river and the formula is used to obtain Eeffective = 5.13 to adapt to the all-round practical detachable powder carrier tank and the all-round practical self-circulating ecological floating ship, so that the device can manage the river basin more accurately and achieve the purpose of purification.

[0145] Step4: The intelligent control center (3) determines the supporting carrier device of the plant powder adsorbent as a self-circulating ecological floating ship or a detachable powder carrier tank according to the above three river basin types;

[0146] Step5: The device manufacturing unit (9) determines the device size according to the river basin type and application scenario and manufactures the device;

[0147] Step6: The intelligent control center (3) uses the dynamic robust cooperative adsorption optimization algorithm DRSAO for the data input in Step1 and Step2, inputs parameters for effective adsorption capacity calculation, then conducts dynamic calculation of the plant powder adsorbent and batch iterative processing, analyzes the cooperative adsorption optimization of multiple metal ions and organic matter particles, and finally obtains the formula of the plant powder adsorbent, as Figure 3 shown, specifically as follows:

[0148] ① Input parameter and effective adsorption capacity calculation

[0149] The heavy metal content and organic matter particle concentration C0 of the water body measured by the water sample analysis unit, and the expected heavy metal content and organic matter particle concentration C of the water body after treatment are formulated according to the national water quality discharge standard t , the volume V of the treated water body is measured, and the maximum adsorption capacity Q of different plant powder adsorbents is input max , and the actual effective adsorption capacity is calculated in combination with the environmental correction factors α, β, γ:

[0150] Among them, the environmental correction factor:

[0151] α is the pH correction coefficient. The best adsorption of the plant powder adsorbent usually occurs in a specific pH range (such as pH = 5 - 6). When deviating, it is necessary to multiply by the correction coefficient α (0 - 1). When the pH deviates from the specific range by greater than or equal to 1.5, take α = 0 - 0.5. When the pH deviates from the specific range by less than 1.5, take α = 0.5 - 1;

[0152] β is the temperature correction coefficient. The adsorption capacity of the plant powder adsorbent is closely related to the temperature. The adsorption efficiency is the best at 25°C. High or low temperatures both inhibit the adsorption. When adsorbing in different temperature environments, it is necessary to multiply by the correction coefficient β (0.8 - 1). When the temperature deviates from 25°C by greater than or equal to 20°C, take β = 0.8 - 0.9. When the temperature deviates from 25°C by less than 20°C, take β = 0.9 - 1;

[0153] γ is the competitive repair inhibition coefficient. The presence of other heavy metal ions or organic matter particles in the water body affects the adsorption capacity of the plant powder adsorbent for specific heavy metal ions or organic matter particles. It is necessary to introduce the competitive inhibition coefficient γ (0 - 1). When there are greater than or equal to 17 other heavy metal ions or organic matter particles in the water body, take γ = 0 - 0.5. When there are less than 17 other heavy metal ions or organic matter particles in the water body, take γ = 0.5 - 1;

[0154] Expand the environmental correction coefficients α, β, γ into a robust optimization model:

[0155]

[0156] s.t.Q effective =Q max ·α·β·γ

[0157] α ∈ [α min , 1], β ∈ [β min , β max , γ ∈ [γ min , 1]

[0158] Dynamic calculation of the plant powder adsorbent:

[0159] Introduce the safety factor k and calculate the mass of the plant powder:

[0160]

[0161] Among them, the safety factor k is used to ensure that the adsorbent can cope with various uncertainties and potential efficiency losses in actual adsorption. Based on engineering experience and experimental data, it aims to ensure the safety and reliability of the system. k takes 1.2 - 2.0. According to the degree, it is divided into mild (k = 1.2 - 1.4), moderate (k = 1.5 - 1.7), and severe (k = 1.8 - 2.0);

[0162] Introduce constraint conditions:

[0163]

[0164] The emission standard constraint refers to the set expected heavy metal content and organic particle concentration C in the treated water body t with the value requirement being less than or equal to the emission standard value C std ;

[0165] The maximum batch limit means that the maximum number of experimental batches set to prevent excessive experimental energy consumption and save costs should be less than or equal to N max ;

[0166] The budget constraint means that the mass M of the newly prepared plant powder adsorbent should be less than or equal to the constraint value M budget , avoiding problems of raw material waste and loss;

[0167] ② Batch iterative treatment

[0168] After single - time treatment, the adsorption capacity of the plant powder adsorbent is affected by various influencing factors, resulting in the water body not reaching the target concentration C t , and then the calculation of the remaining concentration is carried out:

[0169]

[0170] And use C remaining as the new C0 for iteration until it meets the standard;

[0171] Supplement the transfer equation:

[0172]

[0173] The transfer equation is to add a noise term to the iteration equation for calculating the remaining concentration. Among them, the noise term is designed to make the model closer to reality and is used to analyze the robustness of the system, the range of prediction errors, or consider random interference when designing control strategies, avoiding the model becoming completely deterministic and unable to reflect the random behavior of the real system;

[0174] ③ Co - adsorption optimization of multiple metal ions and organic particles

[0175] For water bodies containing multiple heavy metal ions and organic particles, calculate the mass M of the plant powder adsorbent required for adsorbing each metal ion and organic particle respectively i , and take the maximum value M = max(M1, M2, M3,..., M n ) to ensure that the concentrations of all heavy metal ions and organic particles meet the standards,

[0176] Introduce the coupling adsorption matrix A ∈ R m×n, describe the adsorption efficiency of m kinds of plant powder adsorbents for n kinds of heavy metal ions and organic particles:

[0177]

[0178] Constraint:

[0179] A·M≥C req (Meet the constraint)

[0180] Among them, the first item (adsorption effect fitting item): C 0,j Refers to the concentration of the jth pollutant at the initial moment, C t,j Refers to the concentration of the jth pollutant after the treatment time t, Q effective,j Refers to the adsorption efficiency of the plant powder adsorbent for the jth pollutant, C req Is the vector of target concentrations of each metal. This item represents minimizing the difference between the actual removal concentration and the theoretical adsorption efficiency. The sum of squares form aims to make the adsorption effect as close to the expectation as possible;

[0181] The second item (regularization item): Refers to the sum of the squares of all elements of matrix A. ρ is the regularization coefficient, which is used to balance the fitting accuracy and the model complexity. This item prevents the model from overfitting the experimental data, ensures that the element values of the adsorption matrix A are not too large, and enhances the generalization ability;

[0182] Step7: The adsorbent preparation unit 4 prepares the plant powder adsorbent according to the obtained optimal formula;

[0183] Step8: The adsorbent preparation unit 4 puts the prepared adsorbent into the device prepared in Step5 for water treatment;

[0184] Step9: The intelligent control center 3 calculates the time for the plant powder adsorbent to be recycled after reaching the maximum adsorption efficiency through the dynamic economic collaborative recovery optimization algorithm DESRO, determines the maximum adsorption efficiency and the recovery time of the plant powder adsorbent, and transmits the data to the adsorbent recovery unit 12, specifically as follows:

[0185] The plant powder adsorbent can be recycled after reaching the maximum adsorption efficiency. By constructing a "mixed integer nonlinear programming (MINLP)" model, the dosage of the plant powder adsorbent and the recovery time are optimized synchronously:

[0186]

[0187] Among them, the first item is the cost of the adsorbent dosage: M iIt represents the usage amount of the i-th adsorbent. ω1 refers to the weight coefficient of the adsorbent usage amount, with a value ranging from 0.5 to 0.7, which reflects the importance of the adsorbent cost in the overall objective. This term represents minimizing the total usage cost of the adsorbent. If the cost is relatively high, ω1 should take a larger value of 0.6 to 0.7 to reduce the adsorbent usage amount. If the cost is relatively low, ω1 should take a smaller value of 0.5 to 0.6;

[0188] The second item is the recovery time cost: Q effective,i It refers to the adsorption efficiency coefficient of the i-th adsorbent, v 0,i It refers to the initial adsorption efficiency of the i-th adsorbent, s i The recovery time of the i-th adsorbent is a decision variable. ω2 is the weight coefficient of the recovery time, which reflects the importance of the recovery time cost in the overall objective. This term represents minimizing the recovery time cost of all adsorbents. If the time cost is relatively high, ω2 should take a larger value of 0.2 to 0.3 to shorten the recovery time. If the time cost is relatively low, ω2 should take a smaller value of 0.1 to 0.2;

[0189] Constraint condition: M total The upper limit of the total available amount of the adsorbent. The total usage constraint equation (②) is to ensure that the total usage amount of all adsorbents does not exceed the available resources. t max The upper limit of the single recovery time. The recovery time constraint equation (③) is to ensure that the recovery time of each adsorbent does not exceed the allowed maximum value.

[0190] For the water body containing multiple heavy metal ions and organic particles, calculate the time t required for the plant powder adsorbent to adsorb various metal ions and organic particles respectively i , and obtain the average value of the time required to adsorb the water body containing multiple heavy metal ions and organic particles:

[0191]

[0192] Step10: The adsorbent recovery unit 12 regularly replaces and recovers the adsorbent in the device.

[0193] For the sampling point of the tailings water body and the data analysis of the water sample in this embodiment, input the following data into the intelligent control system:

[0194] Sampling water body volume: V = 1000ml, temperature: 20°C, pH value: 6.5;

[0195] Measure C0 of four pollutants:

[0196] Cu 2+ : 1.0mg / L, Zn 2+ : 2.0mg / L, Cr 6+ : 0.1mg / L, PAHs: 0.1mg / L;

[0197] Set the expected concentration C after treatment t :

[0198] Cu 2+ : 0.01 mg / L, Zn 2+ : 0.05 mg / L, Cr 6+ : 0.01 mg / L, PAHs: 0.02 mg / L;

[0199] The maximum adsorption capacity Q of the adsorbent max :

[0200] Cu 2+ : 0.9 mg / g, Zn 2+ : 1.1 mg / g, Cr 6+ : 0.6 mg / g, PAHs: 0.5 mg / g;

[0201] The intelligent control center obtains:

[0202] The pH correction coefficient α: 0.8 (pH = 6.5, deviating from the optimal range by 1.5), the temperature correction coefficient β: 0.9 (temperature = 20 °C, deviating from 25 °C), the competitive remediation inhibition coefficient γ: 0.9 (there are 4 competitive ions), the safety factor k: 1.5 (moderate uncertainty), the budget constraint M budget : 5000 g, the maximum batch limit N max : 5 times;

[0203] Calculate the effective adsorption capacity Q effective :

[0204] Q effective = Q max ·α·β·γ

[0205] Cu 2+ : 0.9×0.8×0.9×0.9 = 0.5832 mg / g

[0206] Zn 2+ : 1.1×0.8×0.9×0.9 = 0.7128 mg / g

[0207] Cr 6+ : 0.6×0.8×0.9×0.9 = 0.3888 mg / g

[0208] PAHs: 0.5×0.8×0.9×0.9 = 0.3240 mg / g

[0209] Calculate the mass M of the adsorbent:

[0210]

[0211]

[0212] Take the maximum value M = 4090 g (satisfying the budget constraint M ≤ 5000 g).

[0213] Process in batches and iterate:

[0214] For the first treatment, use an adsorbent mass M = 4090 g, and the remaining concentration:

[0215]

[0216] The concentrations of all pollutants meet the standards, and no further iteration is required.

[0217] Adsorbent recovery time:

[0218] The adsorption efficiency v0 of the adsorbent in water is 0.05 mg / g / d, and the recovery time:

[0219]

[0220] Average recovery time:

[0221]

[0222] Example 2: The same as Example 1, except that the point-taking unit 1 is increased with drones or remote sensing technology for large-scale and high-precision assisted point-taking, and pH sensors and dissolved oxygen sensors are introduced to obtain more comprehensive water quality data.

[0223] Example 3: The same as Example 1, except that the intelligent control center 3 is increased with an intelligent early warning function. When the water quality data is abnormal, it can automatically trigger the early warning mechanism and notify the relevant personnel for processing in a timely manner.

[0224] The specific embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those of ordinary skill in the art, various changes can be made without departing from the gist of the present invention.

Claims

1. An intelligent integrated targeted plant powder adsorbent water purification system and method, characterized in that, The water purification system includes a sampling unit (1), a water sample analysis unit (2), an intelligent control center (3), an adsorbent preparation unit (4), an environmental analysis unit (5), a device manufacturing unit (6), and an adsorbent recovery unit (7); The sampling unit (1) inputs the natural geographical information, ecological environment information, and human activity information obtained from on-site inspections into the intelligent control center (3). The intelligent control center (3) is connected to the water sample analysis unit (2), the adsorbent preparation unit (4), and the environmental analysis unit (5). The water sample analysis unit (2) is equipped with a full-parameter water quality detector, which transmits the measured heavy metal concentration, nutrient salt concentration, pH value, and soluble pollutant data to the intelligent control center (3). After analyzing the soluble pollutant data, the intelligent control center (3) calculates the required mass of the adsorbent and transmits the data to the adsorbent preparation unit (4) in real time. At the same time, the intelligent control center (3) determines the watershed type through environmental analysis using GIS and satellite maps, and transmits the watershed type data to the environmental analysis unit (5), and also transmits the watershed type data to the device manufacturing unit (6). The device manufacturing unit (6) determines to use the self-circulating ecological floating boat or the detachable powder carrier device of the device manufacturing unit (6) for water purification according to the watershed type. After the water purification is completed, the adsorbent enters the adsorbent recovery unit (7).

2. The intelligent integrated targeted plant powder adsorbent water purification system and method according to claim 1, characterized in that: The specific steps of the water purification system are as follows: Step1: The sampling unit (1) conducts full-range coverage sampling, including the natural geographical information of the sampling points: the watershed area index N1, the narrowest river index N2, the average slope index N3, and the river network density index N4; the ecological environment information: the vegetation coverage index E1, the biodiversity index E2, the water quality pollution index E3, and the wind speed E4; and the human activity information: the population density index H1, the land development intensity index H2, the water resource utilization intensity index H3, and the vessel activity intensity index H4, and inputs the data into the intelligent control center (3); Step2: The full-parameter water quality detector of the water sample analysis unit (2) integrates digestion and measurement, and intelligently monitors the COD, ammonia nitrogen, total phosphorus, total nitrogen content, and pH value. The heavy metal detector in the water quality detector has 360° rotating colorimetric tube detection and colorimetric cuvette detection to accurately monitor the content of heavy metals such as copper, cadmium, zinc, and lead in the water. The water sample analysis unit (2) inputs the measured data into the intelligent control center (3) in real time; Step3: The intelligent control center (3) uses the dynamic multi-dimensional collaborative watershed classification algorithm DMS-WCS to analyze the data input in Step1 and Step2, and obtains three watershed types: the natural geographical index N type, the ecological environment index E type, and the human activity index H type; Step4: The intelligent control center (3) determines the supporting carrier device for the plant powder adsorbent as a self-circulating ecological floating boat or a detachable powder carrier tank according to the above three watershed types; Step5: The device manufacturing unit (9) determines the device size according to the watershed type and application scenario, and manufactures the device; Step6: The intelligent control center (3) uses the dynamic robust collaborative adsorption optimization algorithm DRSAO to calculate the effective adsorption capacity with the input data of Step1 and Step2. Subsequently, dynamic calculation of the plant powder adsorbent and batch iterative processing are carried out to analyze the collaborative adsorption optimization of multiple metal ions and organic particles, and finally the formula of the plant powder adsorbent is obtained; Step7: The adsorbent preparation unit (4) prepares the plant powder adsorbent according to the obtained optimal formula; Step8: The adsorbent preparation unit (4) puts the prepared adsorbent into the device prepared in Step5 for water treatment; Step9: The intelligent control center (3) calculates the time for recycling the plant powder adsorbent after reaching the maximum adsorption efficiency through the dynamic economic collaborative recovery optimization algorithm DESRO, determines the maximum adsorption efficiency and recovery time of the plant powder adsorbent, and transmits the data to the adsorbent recovery unit (12); Step10: The adsorbent recovery unit (12) regularly replaces and recovers the adsorbent in the device.

3. The intelligent integrated targeted plant powder adsorbent water purification system and method according to claim 2, characterized in that: Step3 The calculation of the intelligent control center (3) is as follows: The intelligent control center (3) classifies the water body into three types of index types according to the sampling data of the sampling unit (1) and the water sample analysis unit (2). First, calculate the comprehensive water body pollution index P: Among them, P is the comprehensive water pollution index, ω is the weight coefficient, and its value range is [0, 1]. C k represents the measured concentration of substances after water sample analysis, and S k represents the standard concentration limit of heavy metals in Class V water; According to the P value, then conduct index classification: If P = 0 - 4.5, then the water body is the natural geography index N; if P = 4.5 - 7.0, then the water body is the ecological environment index E; if P > 7.0, then the water body is the human activity index H. After determining the index classification, calculate and correct each index type: ① Natural geography index N type: It consists of the natural geography indexes of the basin area index N1, the narrowest river index N2, the average slope index N3, and the river network density index N4. The calculation formula is: Unify the units of each index, that is, each index is converted to the range of [0, 1]: maxN i and minN i are the maximum and minimum values of N i respectively, and the calculation formula is: Among them, a i is the weight coefficient, and considering the influence of the natural environment, a natural correction coefficient α N = 0.8 - 1.2 is introduced, and the corrected natural geographical index is: N effective = N × α N ; If N effective ∈ [0, 3.0), then the water body is of high ecological integrity and is adapted to a flexible and convenient detachable powder carrier tank; if N effective ∈ [3.0, 4.8) is of medium ecological stability and is adapted to a flexible and convenient detachable powder carrier tank and a flexible and convenient self-circulating ecological floating boat; if N effective ∈ [4.8, +∞) is of mild ecological stress and is adapted to a flexible and convenient self-circulating ecological floating boat; ② Ecological environment index E type: It consists of the ecological environment indexes of the vegetation coverage index E1, the biodiversity index E2, the water quality pollution index E3, and the wind speed E4. Unify the units of them, that is, each index is converted to the range of [0, 1]: maxE i 、minE i are respectively the maximum value and the minimum value of E i , and the calculation formula is as follows: Among them, a i is the weight coefficient introduce the ecological correction coefficient α E ∈(0.7 - 1.3) The corrected ecological environment index is: E effective = E × α E If E effective ∈ [0, 2.8), then the water body is an ecologically coordinated type, and is adapted to an all-purpose practical detachable powder carrier tank; if E effective ∈ [2.8, 5.2), then the water body is a type with balanced human activities, and is adapted to an all-purpose practical detachable powder carrier tank and an all-purpose practical self-circulating ecological floating ship; if E effective ∈ [5.2, +∞), then the water body is a human activity type, and is adapted to an all-purpose practical self-circulating ecological floating ship; ③ Human activity index H type: It consists of the human activity indexes of the population density index H1, the land development intensity index H2, the water resource utilization intensity index H3, and the vessel activity intensity index H4. Unify the units of them, that is, each index is converted to the range of [0, 1]: maxH i and minH i are the maximum and minimum values of H i respectively, and the calculation formula is: Among them, a i is the weight coefficient introduce the human activity correction coefficient α H ∈(0.9 - 1.1), The corrected human activity index is: H effective = H × α H If H effective ∈ [0, 3.2), then the water body is of the human development type and is suitable for a heavy-duty long-distance detachable powder carrier tank; if H effective ∈ [3.2, 4.4), then the water body is of the moderately disturbed type and is suitable for a heavy-duty long-distance detachable powder carrier tank and a heavy-duty long-distance self-circulating ecological floating ship; if H effective ∈ [4.4, +∞), then the water body is of the low-level restoration type and is suitable for an all-purpose practical self-circulating ecological floating ship.

4. An intelligent integrated targeted plant powder adsorbent water purification system and method according to claim 3, characterized in that: In Step6, the intelligent control center (3) uses the dynamic robust collaborative adsorption optimization algorithm DRSAO to calculate the effective adsorption capacity with the input data of Step1 and Step2, as follows: ① Input parameter and effective adsorption capacity calculation The heavy metal content and organic matter particle concentration C0 of the water sample measured by the water sample analysis unit (2), and the expected heavy metal content and organic matter particle concentration C of the treated water are determined according to the national water quality discharge standard t , measure the volume V of the treated water body, and input the maximum adsorption capacity Q of different plant powder adsorbents max , and calculate the actual effective adsorption capacity by combining the environmental correction coefficients α, β, and γ: Among them, the environmental correction coefficient: α is the pH correction coefficient. The best adsorption effect of the plant powder adsorbent usually occurs in a specific pH range. When deviating, the correction coefficient α needs to be multiplied. When the pH deviates from the specific range by greater than or equal to 1.5, take α = 0 - 0.5; when the pH deviates from the specific range by less than 1.5, take α = 0.5 - 1; β is the temperature correction coefficient. The adsorption capacity of the plant powder adsorbent is closely related to the temperature. The adsorption efficiency is the best at 25°C. High or low temperatures both inhibit the adsorption effect. When performing adsorption in different temperature environments, the correction coefficient β needs to be multiplied. When the temperature deviation from 25°C is greater than or equal to 20°C, take β = 0.8 - 0.9; when the temperature deviation from 25°C is less than 20°C, take β = 0.9 - 1; γ is the competitive repair inhibition coefficient. The presence of other heavy metal ions or organic matter particles in the water body affects the adsorption capacity of the plant powder adsorbent for specific heavy metal ions or organic matter particles. The competitive inhibition coefficient γ needs to be introduced. When there are greater than or equal to 17 other heavy metal ions or organic matter particles in the water body, take γ = 0 - 0.5; when there are less than 17 other heavy metal ions or organic matter particles in the water body, take γ = 0.5 - 1; Expand the environmental correction coefficients α, β, and γ into a robust optimization model: s.t.Q effective =Q max ·α·β·γ α ∈ [α min , 1], β ∈ [β min , β max , γ ∈ [γ min , 1] Dynamic calculation of the plant powder adsorbent: Introduce a safety factor k and calculate the mass of the plant powder: Among them, the safety factor k is used to ensure that the adsorbent can cope with various uncertainties and potential efficiency losses in actual adsorption. Based on engineering experience and experimental data, it aims to ensure the safety and reliability of the system. k takes 1.2 - 2.

0. According to the degree, it is divided into mild (k = 1.2 - 1.4), moderate (k = 1.5 - 1.7), and severe (k = 1.8 - 2.0); Introduce constraint conditions: The emission standard constraint refers to the set requirement that the heavy metal content and organic particle concentration C in the treated water body are expected to be less than or equal to the emission standard value C t std ;​ The maximum batch limit refers to that the maximum number of experimental batches set to prevent excessive experimental energy consumption and save costs should be less than or equal to N max ; The budget constraint means that the mass M of the newly prepared plant powder adsorbent should be less than or equal to the constraint value M budget , avoiding problems of raw material waste and loss; ② Batch iterative processing The adsorption capacity of the plant powder adsorbent is affected by various influencing factors, resulting in the water body not reaching the target concentration C after a single treatment. t , and then the calculation of the remaining concentration is carried out: And iterate with C remaining as the new C0 until the standard is met; Supplement the transfer equation: The transfer equation is to add a noise term to the iterative equation for calculating the remaining concentration. Among them, the noise term aims to make the model closer to reality and is used to analyze the robustness of the system, predict the error range, or consider random interference when designing control strategies, so as to avoid the model becoming completely deterministic and unable to reflect the random behavior of the real system; ③ Co - adsorption optimization of multiple heavy metal ions and organic matter particles For a water body containing multiple heavy metal ions and organic particles, calculate the mass M of the plant powder adsorbent required to adsorb each metal ion and organic particle respectively i , and take the maximum value M = max(M1, M2, M3,..., M n ) to ensure that the concentrations of all heavy metal ions and organic particles meet the standards Introduce the coupled adsorption matrix \(A\in\mathbb{R}\) m×n , to describe the adsorption efficiency of \(m\) kinds of plant powder adsorbents for \(n\) kinds of heavy metal ions and organic matter particles: (Regularization prevents overfitting) Constraints: A·M≥C req (Meet the constraint) Among them, the first item (adsorption effect fitting item): C 0,j refers to the concentration of the j-th pollutant at the initial moment, C t,j refers to the concentration of the j-th pollutant after the treatment time t, Q effective,j refers to the adsorption efficiency of the plant powder adsorbent for the j-th pollutant, C req is the vector of target concentrations of each metal. This item represents minimizing the difference between the actual removal concentration and the theoretical adsorption efficiency, and the sum of squares form aims to make the adsorption effect as close as possible to the expectation; Second term (regularization term): It refers to the sum of the squares of all elements of matrix A. ρ is the regularization coefficient, which is used to balance the fitting accuracy and the model complexity. This term prevents the model from overfitting the experimental data, ensures that the element values of the adsorption matrix A are not too large, and enhances the generalization ability.

5. An intelligent integrated targeted plant powder adsorbent water purification system and method according to claim 2, characterized in that: Step9 The recovery of the plant powder adsorbent is specifically as follows: The plant powder adsorbent can be recycled after reaching the maximum adsorption efficiency. By constructing a "Mixed - Integer Nonlinear Programming (MINLP)" model, the dosage of the plant powder adsorbent and the recovery time are optimized synchronously: Among them, the first adsorbent dosage cost: M i represents the usage amount of the i-th adsorbent. ω1 refers to the weight coefficient of the adsorbent dosage, with a value ranging from 0.5 to 0.7, reflecting the importance of the adsorbent cost in the overall goal. This item represents minimizing the total dosage cost of the adsorbent. If the cost is relatively high, ω1 should take a larger value of 0.6 to 0.7 to reduce the adsorbent dosage. If the cost is relatively low, ω1 takes a smaller value of 0.5 to 0.6; The second item is the recovery time cost: Q effective,i Refers to the adsorption efficiency coefficient of the i-th adsorbent, v 0,i Refers to the initial adsorption efficiency of the i-th adsorbent, s i The recovery time of the i-th adsorbent, which is a decision variable. ω2 is the weight coefficient of the recovery time, reflecting the importance of the recovery time cost in the total objective. This item represents minimizing the recovery time costs of all adsorbents. If the time cost is relatively high, ω2 should take a larger value of 0.2 - 0.3 to shorten the recovery time. If the time cost is relatively low, ω2 takes a smaller value of 0.1 - 0.2; Constraint: M total The upper limit of the total available amount of adsorbent. The total usage constraint equation (②) restricts that the total usage of all adsorbents cannot exceed the available resources; t max The upper limit of the single recovery time. The recovery time constraint equation (③) restricts that the recovery time of each adsorbent cannot exceed the allowed maximum value; For a water body containing multiple heavy metal ions and organic particles, calculate the time t required for the plant powder adsorbent to adsorb various metal ions and organic particles respectively i , and obtain the average time required to adsorb the water body containing multiple heavy metal ions and organic particles:

6. The intelligent integrated targeted plant powder adsorbent water purification system and method according to claim 2, characterized in that: The adsorption agent recovery unit (7) in Step10 performs humification treatment on the plant powder - type adsorbent saturated with heavy metals in the self - circulating ecological floating boat or the detachable powder - carrying device put into the basin, realizing heavy metal recovery and obtaining organic fertilizer.

7. An intelligent integrated targeted plant powder adsorbent water purification system and method according to claim 2, characterized in that: The sampling unit (1) adds drones or remote sensing technology for large - range and high - precision auxiliary sampling, and introduces pH sensors and dissolved oxygen sensors to obtain more comprehensive water quality data.

8. According to the intelligent integrated targeted plant powder adsorbent water purification system and method described in claim 2, characterized in that: The intelligent control center (3) adds an intelligent early - warning function. When the water quality data is abnormal, it can automatically trigger the early - warning mechanism and notify relevant personnel for processing in a timely manner.

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