Intelligent optimization system for synergistic remediation of soil microbial community and plants in phytoremediation experimental device

Abstract

The application discloses intelligent optimization of soil microbial community and plant synergistic remediation in a plant remediation experiment device, a system optimizes plant-microbial interaction by real-time monitoring of soil pollutants, microbial metabolites and plant root exudates, using synthetic biology modified microbial sensors and nanorobots, and optogenetic technology, quantum annealing algorithm and federal learning framework are used to generate optimal remediation strategies, bionic mechanical arm and laser-nano technology perform precise operation, microbial fuel cell and piezoelectric energy recovery unit realize system self-energy supply, the system has the characteristics of high efficiency, intelligence and environmental friendliness, and is suitable for remediation of complex soil pollution.

Description

Technical Field

[0001] This invention relates to the field of soil remediation technology, and more specifically, to an intelligent optimization system for the synergistic remediation of soil microbial communities and plants in a phytoremediation experimental apparatus. Background Technology

[0002] With the increasing severity of environmental pollution, plant-microbe co-remediation technology has received widespread attention as an efficient and environmentally friendly soil remediation method. This technology utilizes the interaction between plants and microorganisms, using plant root exudates to provide nutrients for microorganisms, while microorganisms remove pollutants from the soil through degradation and transformation, thereby achieving soil remediation. However, traditional plant-microbe remediation technology suffers from problems such as low remediation efficiency and difficulty in precisely controlling the remediation process.

[0003] In recent years, the continuous development of cutting-edge technologies such as big data analysis, artificial intelligence, nanotechnology, and synthetic biology has made it possible to upgrade plant-microbe synergistic remediation technology to an intelligent level. For example, big data analysis can monitor the dynamic data of soil pollutant concentrations, microbial metabolites, and plant root exudates in real time, providing data support for precise regulation of the remediation process. Artificial intelligence algorithms, such as causal inference and quantum annealing algorithms, can optimize remediation strategies and improve remediation efficiency. The combination of nanotechnology and synthetic biology can achieve precise regulation and functional enhancement of microorganisms. The integrated application of these technologies has promoted the development of plant-microbe synergistic remediation technology towards intelligence, efficiency, and precision, providing new methods for solving soil pollution problems.

[0004] Existing technologies suffer from problems such as insufficient data collection, imprecise microbial regulation, suboptimal remediation strategies, and reliance on external energy sources. Summary of the Invention

[0005] To overcome problems such as insufficient data collection, inaccurate microbial regulation, suboptimal remediation strategies, and reliance on external energy sources, this invention designs an intelligent optimization system for the synergistic remediation of soil microbial communities and plants in a phytoremediation experimental device, which can effectively solve the above-mentioned technical problems.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] The intelligent optimization system for the synergistic remediation of soil microbial communities and plants in the phytoremediation experimental device includes:

[0008] The multi-source data acquisition module is used to collect real-time dynamic data on soil pollutant concentrations, rhizosphere microbial single-cell metabolites, and plant root exudates.

[0009] The microbial intelligent regulation module is used to trigger targeted degradation through synthetic biology-modified microbial sensors and control the delivery of functional bacterial agents by nanorobots;

[0010] The plant dynamic regulation module is used to enhance plant-microbe signal interactions based on optogenetics and regulate the root biomimetic structure to guide microbial colonization.

[0011] The quantum optimization decision module is used to integrate causal inference and quantum annealing algorithm to generate the globally optimal repair strategy;

[0012] The biomimetic execution control module is used to perform laser pulse activation, precise delivery of nanorobots, and dynamic adjustment of the biomimetic root scaffold.

[0013] The self-powered management module drives the system through a microbial fuel cell.

[0014] The holographic visualization module is used to display real-time holographic projections of pollutant degradation pathways, microbial community dynamics, and plant physiological states.

[0015] Preferably, the multi-source data acquisition module includes:

[0016] The single-cell metabolomics monitoring unit integrates a microfluidic chip and a high-resolution time-of-flight mass spectrometer to capture rhizosphere single-cell microorganisms and detect their metabolites, thereby constructing a metabolic network.

[0017] The pollutant gradient sensing unit deploys nanoscale heavy metal sensors and polycyclic aromatic hydrocarbon fluorescent probes to generate three-dimensional thermal maps of soil pollution.

[0018] The root exudate dynamic collection unit uses a micro-permeable membrane probe to collect root exudates and perform component analysis.

[0019] Preferably, the single-cell metabolomics monitoring unit specifically includes:

[0020] The microfluidic sorting chip subunit integrates an inertial focusing channel and a dielectrophoretic trap for capturing single cells;

[0021] The in-situ lysis module subunit utilizes the laser-induced cavitation effect to achieve cell wall rupture.

[0022] Metabolite real-time association subunits are used to establish association graphs using graph attention network algorithms.

[0023] Preferably, the intelligent microbial regulation module includes:

[0024] The photo-controlled microbial sensor unit edits the genes of microorganisms to make them express photosensitive degradation gene clusters, which can activate the degradation pathway under laser irradiation;

[0025] Magnetic nanodelivery units are used to construct core-shell structured nanorobots, load functional bacteria, and guide them to the core of the contamination area via a magnetic field.

[0026] The quorum sensing interference unit designs and synthesizes analogs to interfere with the quorum sensing signal pathway of pathogenic bacteria and enhance the formation of biofilms of degrading bacteria. The types of analogs include: AHL analogs, AIP analogs and AI-2 analogs.

[0027] Preferably, the magnetic nanodelivery unit specifically includes:

[0028] The targeted localization algorithm subunit plans the motion path of the nanorobot based on the pollution heat map and soil hydraulic conductivity model;

[0029] pH / ROS dual-response subunit, nanocarrier decomposes and releases bacterial agent under specific conditions;

[0030] The self-healing shell material subunit uses a dynamically disulfide-linked polymer to maintain structural integrity.

[0031] Preferably, the plant dynamic regulation module includes:

[0032] Root optogenetic units express light-activated proton pumps in plant roots, promoting the secretion of root exudates;

[0033] The 4D-printed biomimetic scaffold unit uses a temperature-sensitive hydrogel material to create a porous biomimetic root structure to guide microbial colonization.

[0034] Metabolic reprogramming units regulate the expression of plant metallothionein genes, enhancing the plant's ability to chelate pollutants.

[0035] Preferably, the quantum optimization decision module includes:

[0036] The causal graph model unit uses the FCI algorithm to construct a causal network and identify key regulatory targets.

[0037] The quantum annealing optimization unit encodes the multi-objective optimization problem into a qubit Ising model and solves for the Pareto optimal solution.

[0038] Federated learning collaborative units construct a federated learning framework across remediation sites, enabling secure sharing of model parameters.

[0039] Preferably, the quantum annealing optimization unit specifically includes:

[0040] The hybrid coding strategy subunit discretizes continuous variables into binary codes and maps them to the quantum bit chain;

[0041] The annealing scheduling optimizer subunit uses a reverse annealing strategy to avoid local optima.

[0042] The classical post-processing module subunit fine-tunes the quantum solution using a simulated annealing algorithm.

[0043] Preferably, the biomimetic execution control module includes:

[0044] The laser-nano synergistic unit uses a femtosecond laser to instantly penetrate the outer shell of the nanorobot, releasing bacterial agents and activating degradation genes;

[0045] The biomimetic robotic arm unit is designed based on biomimetic principles, featuring a flexible robotic arm and a biomimetic root support.

[0046] The dynamic drip irrigation unit precisely regulates the release of nutrient solution based on the decision results.

[0047] Preferably, the self-powered management module includes:

[0048] The battery unit uses a biofilm to degrade root exudates and utilizes plant photosynthesis to produce oxygen and output electrical energy.

[0049] The energy routing controller unit dynamically distributes electrical energy to each module and controls leakage current;

[0050] The piezoelectric energy recovery unit generates piezoelectric charges through the deformation of a biomimetic root support to replenish the system's energy consumption.

[0051] Compared with existing technologies, the beneficial effects of this invention are as follows: The microbial intelligent regulation module utilizes synthetic biology-modified microbial sensors and nanorobots to deliver functional bacterial agents, achieving precise regulation of microorganisms and solving the problem of inaccurate microbial regulation; the light-controlled microbial sensor unit uses gene editing to enable microorganisms to express photosensitive degradation gene clusters, which can initiate degradation pathways under laser irradiation; the magnetic nanodelivery unit constructs core-shell structured nanorobots, loads functional bacteria, and guides them to the core of the contamination area through a magnetic field, ensuring precise delivery of functional bacterial agents; the quorum sensing interference unit designs and synthesizes analogues to interfere with the quorum sensing signal pathway of pathogenic bacteria, enhancing the formation of biofilms by degrading bacteria and improving the degradation efficiency and stability of microorganisms; the quantum optimization decision module integrates causal inference and quantum annealing algorithms, dynamically adjusting the repair scheme based on real-time data to generate a globally optimal repair strategy, overcoming the defect of non-optimal repair strategies, because this module can comprehensively consider multiple factors through causal graph modeling. The system employs a multi-unit approach to identify key regulatory targets, a quantum annealing optimization unit to solve for Pareto optimal solutions, and a federated learning collaborative unit to ensure secure sharing of model parameters, achieving dynamic optimization and global optimization of the remediation strategy. A self-powered management module converts the chemical energy generated by the degradation of root exudates into electrical energy through a microbial fuel cell, while simultaneously utilizing plant photosynthetic oxygen production and piezoelectric charges generated by the deformation of the biomimetic root support to supplement system energy consumption, achieving energy self-sufficiency and eliminating dependence on external energy sources. This module efficiently converts bioenergy into electrical energy and dynamically distributes electrical energy to each module through an energy routing controller unit, while controlling leakage current to ensure stable system operation. A biomimetic execution control module performs laser pulse activation, precise nanorobot deployment, and dynamic adjustment of the biomimetic root support, further improving remediation efficiency and accuracy. A holographic visualization module displays real-time holographic projections of pollutant degradation paths, microbial community dynamics, and plant physiological states, providing visualization support for monitoring and optimizing the remediation process. Attached Figure Description

[0052] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other embodiments can be derived from the provided drawings without creative effort.

[0053] Figure 1 This is a structural diagram of an intelligent optimization system for the synergistic remediation of soil microbial communities and plants in a phytoremediation experimental device. Detailed Implementation

[0054] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent.

[0055] To better illustrate this embodiment, some parts in the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions;

[0056] It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.

[0057] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0058] Example

[0059] The multi-source data acquisition module deploys a sensor network in areas of heavy metal-contaminated soil, including nanoscale heavy metal sensors, to monitor the concentration of heavy metals in the soil in real time; it sets up micro-permeable membrane probes to collect plant root exudates and is equipped with a high-resolution time-of-flight mass spectrometer for component analysis; and it installs microfluidic chip devices in the plant rhizosphere region to capture single-celled microorganisms and detect their metabolites.

[0060] The microbial intelligent regulation module selects microbial strains suitable for degrading heavy metals, such as Pseudomonas and arbuscular mycorrhizal fungi, and edits their genes to express photosensitive degradation gene clusters; it constructs Fe3O4@MOF core-shell nanorobots, loads functional bacteria, and controls their movement path through an electromagnetic navigation matrix.

[0061] The plant dynamic regulation module selects suitable plant varieties for remediation of heavy metal pollution, such as Centipede Grass and Sedum aizoon, and expresses photoactivated proton pumps in their roots to promote the secretion of root exudates; it uses temperature-sensitive hydrogel materials to create porous biomimetic root structures to guide microbial colonization; and it uses CRISPR-dCas9 technology to regulate the expression of plant metallothionein genes to enhance the plant's chelation ability for pollutants.

[0062] The quantum optimization decision module establishes a database containing data on soil pollutant concentrations, microbial metabolites, and plant root exudates. It uses the FCI algorithm to construct a causal network and identify key regulatory targets. The multi-objective optimization problem is encoded as a qubit Ising model, and the Pareto optimal solution is obtained using the quantum annealing algorithm. A federated learning framework is constructed across remediation sites to achieve secure sharing of model parameters.

[0063] The biomimetic execution control module, equipped with a femtosecond laser device, is used to instantly penetrate the shell of the nanorobot, release the bacterial agent, and activate the degradation genes; a flexible robotic arm based on biomimetic principles is designed to deploy a biomimetic root support; and a dynamic drip irrigation system is installed to precisely control the release of nutrient solution based on the decision results.

[0064] The self-powered management module is equipped with a microbial fuel cell that uses Geobacter biofilm to degrade root exudates and output electrical energy; it is also equipped with an energy routing controller to dynamically distribute electrical energy to each module and control leakage current; and it is equipped with a piezoelectric energy recovery device that generates piezoelectric charge through the deformation of the biomimetic root support to supplement the system's energy consumption.

[0065] The holographic visualization module builds a holographic projection system to display pollutant degradation pathways, microbial community dynamics, and plant physiological states in real time.

[0066] A smart optimization system for the synergistic remediation of soil microbial communities and plants in a phytoremediation experimental device, such as Figure 1 As shown, it includes:

[0067] The multi-source data acquisition module is used to collect real-time dynamic data on soil pollutant concentrations, rhizosphere microbial single-cell metabolites, and plant root exudates.

[0068] The microbial intelligent regulation module is used to trigger targeted degradation through synthetic biology-modified microbial sensors and control the delivery of functional bacterial agents by nanorobots;

[0069] The plant dynamic regulation module is used to enhance plant-microbe signal interactions based on optogenetics and regulate the root biomimetic structure to guide microbial colonization.

[0070] The quantum optimization decision module is used to integrate causal inference and quantum annealing algorithm to generate the globally optimal repair strategy;

[0071] The biomimetic execution control module is used to perform laser pulse activation, precise delivery of nanorobots, and dynamic adjustment of the biomimetic root scaffold.

[0072] The self-powered management module drives the system through a microbial fuel cell.

[0073] The holographic visualization module is used to display real-time holographic projections of pollutant degradation pathways, microbial community dynamics, and plant physiological states.

[0074] The multi-source data acquisition module includes:

[0075] The single-cell metabolomics monitoring unit integrates a microfluidic chip and a high-resolution time-of-flight mass spectrometer to capture rhizosphere single-cell microorganisms and detect their metabolites, thereby constructing a metabolic network.

[0076] The pollutant gradient sensing unit deploys nanoscale heavy metal sensors and polycyclic aromatic hydrocarbon fluorescent probes to generate three-dimensional thermal maps of soil pollution.

[0077] The root exudate dynamic collection unit uses a micro-permeable membrane probe to collect root exudates and perform component analysis.

[0078] The single-cell metabolomics monitoring unit specifically includes:

[0079] The microfluidic sorting chip subunit integrates an inertial focusing channel and a dielectrophoretic trap for capturing single cells;

[0080] The in-situ lysis module subunit utilizes the laser-induced cavitation effect to achieve cell wall rupture.

[0081] Metabolite real-time association subunits are used to establish association graphs using graph attention network algorithms.

[0082] The intelligent microbial regulation module includes:

[0083] The photo-controlled microbial sensor unit edits the genes of microorganisms to make them express photosensitive degradation gene clusters, which can activate the degradation pathway under laser irradiation;

[0084] Magnetic nanodelivery units are used to construct core-shell structured nanorobots, load functional bacteria, and guide them to the core of the contamination area via a magnetic field.

[0085] The quorum sensing interference unit designs and synthesizes analogs to interfere with the quorum sensing signal pathway of pathogenic bacteria and enhance the formation of biofilms of degrading bacteria. The types of analogs include: AHL analogs, AIP analogs and AI-2 analogs.

[0086] The magnetic nanodelivery unit specifically includes:

[0087] The targeted localization algorithm subunit plans the motion path of the nanorobot based on the pollution heat map and soil hydraulic conductivity model;

[0088] pH / ROS dual-response subunit, nanocarrier decomposes and releases bacterial agent under specific conditions;

[0089] The self-healing shell material subunit uses a dynamically disulfide-linked polymer to maintain structural integrity.

[0090] The plant dynamic regulation module includes:

[0091] Root optogenetic units express light-activated proton pumps in plant roots, promoting the secretion of root exudates;

[0092] The 4D-printed biomimetic scaffold unit uses a temperature-sensitive hydrogel material to create a porous biomimetic root structure to guide microbial colonization.

[0093] Metabolic reprogramming units regulate the expression of plant metallothionein genes, enhancing the plant's ability to chelate pollutants.

[0094] The quantum optimization decision module includes:

[0095] The causal graph model unit uses the FCI algorithm to construct a causal network and identify key regulatory targets.

[0096] The quantum annealing optimization unit encodes the multi-objective optimization problem into a qubit Ising model and solves for the Pareto optimal solution.

[0097] Federated learning collaborative units construct a federated learning framework across remediation sites, enabling secure sharing of model parameters.

[0098] The quantum annealing optimization unit specifically includes:

[0099] The hybrid coding strategy subunit discretizes continuous variables into binary codes and maps them to the quantum bit chain;

[0100] The annealing scheduling optimizer subunit uses a reverse annealing strategy to avoid local optima.

[0101] The classical post-processing module subunit fine-tunes the quantum solution using a simulated annealing algorithm.

[0102] The biomimetic execution control module includes:

[0103] The laser-nano synergistic unit uses a femtosecond laser to instantly penetrate the outer shell of the nanorobot, releasing bacterial agents and activating degradation genes;

[0104] The biomimetic robotic arm unit is designed based on biomimetic principles, featuring a flexible robotic arm and a biomimetic root support.

[0105] The dynamic drip irrigation unit precisely regulates the release of nutrient solution based on the decision results.

[0106] The self-powered management module includes:

[0107] The battery unit uses a biofilm to degrade root exudates and utilizes plant photosynthesis to produce oxygen and output electrical energy.

[0108] The energy routing controller unit dynamically distributes electrical energy to each module and controls leakage current;

[0109] The piezoelectric energy recovery unit generates piezoelectric charges through the deformation of a biomimetic root support to replenish the system's energy consumption.

[0110] During actual operation, after the system starts, the multi-source data acquisition module collects dynamic data on the concentration of heavy metals in the soil, rhizosphere microbial single-cell metabolites, and plant root exudates in real time, and transmits them to the quantum optimization decision module. For example, the nanoscale heavy metal sensor collects heavy metal concentration data in the soil every 10 minutes, and the micro-permeable membrane probe collects root exudate samples for component analysis every 2 hours.

[0111] Based on the received data, the microbial intelligent regulation module activates the degradation pathway of microorganisms under laser irradiation through the light-controlled microbial sensor unit, enabling microorganisms such as Pseudomonas to efficiently degrade heavy metals. At the same time, the magnetic nanodelivery unit precisely delivers nanorobots loaded with functional bacteria to the core pollution area according to the path planned by the pollution heat map and soil hydraulic conductivity model, enhancing the microorganisms' tolerance and degradation ability to heavy metals.

[0112] The plant dynamic regulation module promotes the secretion of more organic acids, such as citric acid and malic acid, by the root optogenetic unit under blue light stimulation. These organic acids can increase the availability of heavy metals in the soil and attract more microorganisms to the roots. The 4D-printed biomimetic scaffold unit provides a suitable colonization environment for microorganisms and guides them to form a stable biofilm around the roots. The metabolic reprogramming unit improves the plant's ability to absorb and chelate pollutants by regulating the expression of plant metallothionein genes.

[0113] The quantum optimization decision-making module performs in-depth analysis and processing of the collected data, constructs a causal network using the FCI algorithm, and identifies key factors affecting soil remediation effectiveness, such as soil pH and microbial community structure. Then, the multi-objective optimization problem is encoded into a qubit Ising model, and the Pareto optimal solution is obtained using the quantum annealing algorithm to generate a globally optimal remediation strategy, including parameters such as plant density, microbial deployment, and light intensity. The federated learning collaborative unit enables secure sharing of model parameters across remediation sites and continuously optimizes the decision-making model.

[0114] The biomimetic execution control module, based on the repair strategy generated by the quantum optimization decision module, precisely activates the microbial degradation genes in the nanorobot through the laser-nano synergistic unit, enabling it to efficiently degrade heavy metals in the soil; the biomimetic robotic arm unit precisely deploys 4D-printed biomimetic root scaffolds around the plant roots, providing attachment sites for microorganisms; and the dynamic drip irrigation unit precisely regulates the release of nutrient solution based on the decision results, providing suitable growth conditions for plants and microorganisms.

[0115] The self-powered management module converts the chemical energy generated by the degradation of root exudates into electrical energy through a microbial fuel cell, providing partial energy support for system operation; the energy routing controller unit dynamically distributes electrical energy to each module and controls leakage current to ensure stable system operation; the piezoelectric energy recovery unit generates piezoelectric charges through the deformation of the biomimetic root support to further supplement the system's energy consumption.

[0116] The holographic visualization module displays in real time the degradation pathways of heavy metals in the soil, the dynamic changes of the microbial community, and the physiological state of plants, such as root growth and leaf photosynthetic efficiency, providing researchers with intuitive information on the remediation process and facilitating timely adjustments to remediation strategies.

[0117] Regularly inspect and calibrate equipment such as sensors and mass spectrometers to ensure the accuracy and reliability of data acquisition. For example, calibrate the heavy metal sensor every two weeks and maintain and adjust the time-of-flight mass spectrometer monthly. Based on soil pollution levels and microbial community dynamics, supplement with appropriate amounts of microbial agents to maintain the stability and degradation capacity of the microbial community. For example, supplement with functional microbial agents that degrade heavy metals every three months, with the amount determined based on the residual heavy metal levels in the soil and the activity of the microorganisms. Regularly manage plants through pruning and fertilization to promote healthy growth. Harvest mature plant biomass promptly and dispose of it properly, such as through composting, to reduce secondary pollution. Establish a system fault early warning mechanism to promptly detect and eliminate faults in system operation. For example, when the energy routing controller detects excessive leakage current, immediately activate the fault alarm, check the wiring connections and battery units, and repair the fault point promptly.

[0118] The same or similar labels correspond to the same or similar parts;

[0119] The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent.

[0120] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all implementation methods here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. An intelligent optimization system for the synergistic remediation of soil microbial communities and plants in a phytoremediation experimental device, characterized in that, include: The multi-source data acquisition module is used to collect real-time dynamic data on soil pollutant concentrations, rhizosphere microbial single-cell metabolites, and plant root exudates. The microbial intelligent regulation module is used to trigger targeted degradation through synthetic biology-modified microbial sensors and control the delivery of functional bacterial agents by nanorobots; The plant dynamic regulation module is used to enhance plant-microbe signal interactions based on optogenetics and regulate the root biomimetic structure to guide microbial colonization. The quantum optimization decision module is used to integrate causal inference and quantum annealing algorithm to generate the globally optimal repair strategy; The biomimetic execution control module is used to perform laser pulse activation, precise delivery of nanorobots, and dynamic adjustment of the biomimetic root scaffold. The self-powered management module drives the system through a microbial fuel cell. The holographic visualization module is used to display holographic projections of pollutant degradation pathways, microbial community dynamics, and plant physiological states in real time. The multi-source data acquisition module includes: The single-cell metabolomics monitoring unit integrates a microfluidic chip and a high-resolution time-of-flight mass spectrometer to capture rhizosphere single-cell microorganisms and detect their metabolites, thereby constructing a metabolic network. The pollutant gradient sensing unit deploys nanoscale heavy metal sensors and polycyclic aromatic hydrocarbon fluorescent probes to generate three-dimensional thermal maps of soil pollution. The root exudate dynamic collection unit uses a micro-permeable membrane probe to collect root exudates and perform component analysis; The single-cell metabolomics monitoring unit specifically includes: The microfluidic sorting chip subunit integrates an inertial focusing channel and a dielectrophoretic trap for capturing single cells; The in-situ lysis module subunit utilizes the laser-induced cavitation effect to achieve cell wall rupture. Metabolite real-time association subunits are used to establish association graphs using graph attention network algorithms; The intelligent microbial regulation module includes: The photo-controlled microbial sensor unit edits the genes of microorganisms to make them express photosensitive degradation gene clusters, which can activate the degradation pathway under laser irradiation; Magnetic nanodelivery units are used to construct core-shell structured nanorobots, load functional bacteria, and guide them to the core of the contamination area via a magnetic field. A quorum sensing interference unit designs and synthesizes analogues to interfere with the quorum sensing signal pathway of pathogenic bacteria and enhance the formation of biofilms of degrading bacteria. The types of analogues include: AHL analogues, AIP analogues and AI-2 analogues. The magnetic nanodelivery unit specifically includes: The targeted localization algorithm subunit plans the motion path of the nanorobot based on the pollution heat map and soil hydraulic conductivity model; pH / ROS dual-response subunit, nanocarrier decomposes and releases bacterial agent under specific conditions; The self-healing shell material subunit uses a dynamically disulfide-linked polymer to maintain structural integrity.

2. The intelligent optimization system according to claim 1, characterized in that, The plant dynamic regulation module includes: Root optogenetic units express light-activated proton pumps in plant roots, promoting the secretion of root exudates; The 4D-printed biomimetic scaffold unit uses a temperature-sensitive hydrogel material to create a porous biomimetic root structure to guide microbial colonization. Metabolic reprogramming units regulate the expression of plant metallothionein genes, enhancing the plant's ability to chelate pollutants.

3. The intelligent optimization system according to claim 1, characterized in that, The quantum optimization decision module includes: The causal graph model unit uses the FCI algorithm to construct a causal network and identify key regulatory targets. The quantum annealing optimization unit encodes the multi-objective optimization problem into a qubit Ising model and solves for the Pareto optimal solution. Federated learning collaborative units construct a federated learning framework across remediation sites, enabling secure sharing of model parameters.

4. The intelligent optimization system according to claim 3, characterized in that, The quantum annealing optimization unit specifically includes: The hybrid coding strategy subunit discretizes continuous variables into binary codes and maps them to the quantum bit chain; The annealing scheduling optimizer subunit uses a reverse annealing strategy to avoid local optima. The classical post-processing module subunit fine-tunes the quantum solution using a simulated annealing algorithm.

5. The intelligent optimization system according to claim 1, characterized in that, The biomimetic execution control module includes: The laser-nano synergistic unit uses a femtosecond laser to instantly penetrate the outer shell of the nanorobot, releasing bacterial agents and activating degradation genes; The biomimetic robotic arm unit is designed based on biomimetic principles, featuring a flexible robotic arm and a biomimetic root support. The dynamic drip irrigation unit precisely regulates the release of nutrient solution based on the decision results.

6. The intelligent optimization system according to claim 1, characterized in that, The self-powered management module includes: The battery unit uses a biofilm to degrade root exudates and utilizes plant photosynthesis to produce oxygen and output electrical energy. The energy routing controller unit dynamically distributes electrical energy to each module and controls leakage current; The piezoelectric energy recovery unit generates piezoelectric charges through the deformation of a biomimetic root support to replenish the system's energy consumption.

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