Local plant community configuration and dynamic optimization system suitable for combined pollution site

Abstract

The invention relates to an indigenous plant community configuration and dynamic optimization system suitable for a combined pollution site, and belongs to the technical field of environmental ecological restoration and soil pollution treatment. Comprising an indigenous plant resource database module, a pollution site information acquisition and processing module, a plant community intelligent configuration module, a real-time monitoring and data feedback module, a dynamic optimization decision module and a visual interaction module. According to the system, intelligent screening and community construction of indigenous plants are achieved through multi-source data fusion and a multi-target optimization algorithm, a sensor network and a machine learning model are combined to conduct real-time monitoring and dynamic regulation and control on the remediation process, the remediation efficiency, community stability and ecological safety of a heavy metal and organic pollutant combined pollution site are improved, and the remediation cost is lowered. And full-period intelligent, low-cost, green and sustainable ecological restoration management is supported.

Description

Technical Field

[0001] This invention relates to the field of environmental ecological restoration and soil pollution remediation technology, and in particular to a native plant community configuration and dynamic optimization system suitable for sites with complex pollution. This system comprehensively utilizes phytoremediation, community ecology, environmental soil science, and information technology to intelligently select native plant species, scientifically construct functional communities, and monitor and adaptively optimize the remediation process in real time, tailored to the characteristics of sites with complex pollution such as heavy metals and organic matter. Background Technology

[0002] With the rapid development of industrialization and urbanization, and the adjustment of industrial structure, many sites left behind by the relocation of industrial enterprises have serious problems of complex soil pollution. These sites are often simultaneously polluted by multiple heavy metals (such as lead, cadmium, arsenic, and mercury) and organic pollutants (such as polycyclic aromatic hydrocarbons, petroleum hydrocarbons, and pesticides), posing high environmental risks, great difficulty in remediation, and potential threats to the surrounding ecosystems and human health.

[0003] Traditional physical and chemical remediation techniques (such as topsoil replacement, leaching, and thermal desorption) are effective, but they often have drawbacks such as high cost, large engineering workload, high risk of secondary pollution, and damage to the soil ecosystem. In contrast, phytoremediation technology is considered a green and sustainable remediation technology with broad application prospects due to its advantages such as low cost, environmental friendliness, in-situ implementation, and ability to beautify the landscape.

[0004] However, applying phytoremediation technology to actual multi-contaminated sites still faces many technical bottlenecks:

[0005] Existing research largely focuses on hyperaccumulating plants capable of accumulating single pollutants. However, these plants often have small biomass, long growth cycles, poor stress resistance, and difficulty in simultaneously tolerating or absorbing multiple pollutants. For complex pollution scenarios with multiple pollutants coexisting, the remediation effect of a single species is very limited.

[0006] Current phytoremediation practices often involve simply piecing together a few plant species, lacking a scientific configuration based on the principles of niche complementarity and functional synergy. A stable and efficient functional plant community should simultaneously include enriching plants, barrier plants, and rhizosphere degrading plants, maximizing remediation efficacy and maintaining ecosystem stability through interspecific interactions. How to scientifically configure native plant communities with synergistic remediation effects based on the type, concentration, and spatial distribution of pollutants remains a current technical challenge.

[0007] Most existing remediation plans are fixed once determined at the beginning of the project, lacking continuous monitoring and dynamic feedback on the remediation process. Factors such as plant growth, pollutant migration and transformation, and changes in climate conditions are all uncertain. A static plan cannot adjust and optimize itself based on on-site feedback, which may lead to low remediation efficiency or even failure.

[0008] Compared to invasive species, native plants possess natural adaptability and strong resistance to local climate and soil conditions, making them easier to survive and form stable communities, while avoiding the ecological risks of biological invasion. However, a systematic approach and technical framework have yet to be established for fully exploring and utilizing native plant resources and selecting the most suitable species combinations for specific contaminated sites.

[0009] Therefore, there is an urgent need in this field for an intelligent system that can integrate native plant resource databases, multi-objective optimization algorithms, real-time environmental monitoring technology and dynamic decision-making models to achieve full-cycle scientific management of plant communities for the remediation of complex contaminated sites, from "precise design" to "process optimization," to overcome the shortcomings of existing technologies and improve the efficiency, stability and sustainability of phytoremediation.

[0010] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0011] The purpose of this invention is to provide an intelligent system that integrates a native plant resource database, multi-objective optimization algorithms, real-time environmental monitoring technology, and dynamic decision-making models to achieve scientific, intelligent, and dynamic management of the entire process of phytoremediation of complex contaminated sites.

[0012] To achieve the above objectives, this invention provides a native plant community configuration and dynamic optimization system suitable for complex contaminated sites. This system consists of six core modules, forming a closed-loop intelligent remediation management system:

[0013] A system for configuring and dynamically optimizing native plant communities suitable for sites with complex contamination includes:

[0014] The native plant resource database module is used to store plant attribute data containing a variety of native plants. The plant attribute data includes plant species, growth characteristics, stress resistance, accumulation capacity, degradation capacity, root characteristics, suitable habitat conditions, and tolerance and remediation efficiency data to heavy metals and organic pollutants.

[0015] This module constructs a database of native plants covering major ecological regions across the country, currently containing over 500 native plant species with restoration potential. Each plant record includes more than 30 attribute data points, such as plant species, growth cycle, growth amount, root type, stress resistance, enrichment coefficients and degradation efficiency of heavy metals (e.g., Cd, Pb, As, Hg) and organic pollutants (e.g., PAHs, TPHs), and suitable habitat parameters (e.g., pH range, soil texture, water requirements). Furthermore, the module integrates a plant image library, phenological data, geographical distribution information, and interaction characteristics with rhizosphere microorganisms, supporting multi-condition combined queries and intelligent recommendations.

[0016] The contaminated site information acquisition and processing module is used to acquire environmental data of the target contaminated site, including soil type, pH value, pollutant type, concentration and spatial distribution, moisture conditions, and climate data. It preprocesses and performs spatial interpolation analysis on the environmental data to generate a pollution distribution map and environmental factor layers. This module uses multi-source data fusion methods, including ground sampling, sensor deployment, UAV aerial photography, and satellite remote sensing, to acquire information such as soil physicochemical properties, spatial distribution of pollutant concentrations (sampling point density up to one point every 10 meters), climate data, and topography of the contaminated site. Using a Geographic Information System (GIS) and spatial interpolation algorithms, it generates high-precision pollution distribution maps, ecological risk level maps, and environmental light factor layers, providing a data foundation for subsequent vegetation configuration.

[0017] The intelligent plant community configuration module, based on the native plant resource database and contaminated site information, employs a multi-objective optimization algorithm. With remediation efficiency, community stability, ecological safety, and cost-effectiveness as optimization objectives, and considering constraints (such as soil pH and water availability), it outputs an initial community configuration scheme containing various native plants. This scheme includes species composition (e.g., a combination of *Pennisetum alopecuroides*, *Centipeda minima*, and alfalfa) and planting density (e.g., 8 *Pennisetum alopecuroides* plants / m²). 2 Spatial layout (patchy mixed planting) and functional group structure (60% enriching plants, 20% barrier plants, and 20% rhizosphere degrading plants);

[0018] The optimization objective function used in this module is expressed as:

[0019] maxF(X)=[f1(X),f2(X),f3(X),f4(X)];

[0020] Where X represents the plant configuration scheme, including species selection, density and spatial layout;

[0021] f1(X) represents the repair efficiency, defined as:

[0022]

[0023] Where, α i C represents the enrichment or degradation coefficient of the target pollutant by the i-th plant species. i For its planting density, B i The capacity to repair per unit of biomass;

[0024] f2(X) represents community stability, which can be measured by the species diversity index:

[0025]

[0026] Where S is the number of species, p i This represents the relative biomass percentage of the i-th species.

[0027] f3(X) represents ecological security, taking into account the plant's ability to control the migration of pollutants;

[0028] f4(X) represents the cost-effectiveness, including the total cost of planting, maintenance, and monitoring.

[0029] The real-time monitoring and data feedback module includes a sensor network deployed at the contaminated site, comprising soil moisture sensors (measurement range 0-100%), pH sensors (3-9), heavy metal ion selective electrodes (detection limit 0.01 mg / kg), polycyclic aromatic hydrocarbon fluorescence sensors (detection limit 0.1 μg / kg), a small weather station (monitoring temperature, humidity, rainfall, and light), and drones periodically conducting aerial photography to monitor plant canopy coverage. Data is transmitted to the platform every 6 hours via LoRaWAN for initial clarification and fusion processing. The sensor network continuously monitors plant growth status, soil physicochemical properties, pollutant concentration changes, and meteorological conditions, and transmits the monitoring data wirelessly to the central processing unit.

[0030] The dynamic optimization decision module receives the real-time monitoring data, combines it with preset restoration goals and constraints, and uses adaptive algorithms and machine learning models to dynamically adjust the plant community configuration, generating optimization instructions, including species replacement suggestions, planting density adjustments, and nutrient and water management strategies.

[0031] The dynamic adjustment strategy adopted by this module can be modeled as follows:

[0032] X t+1 =X t +ΔX t ;

[0033] Among them, X t For the current configuration, ΔX t The adjustment amount, based on monitoring data and the output of the prediction model, is generated according to the following rules:

[0034]

[0035] Where, j(X) t ) represents the function for evaluating the repair effect under the current configuration, and η represents the learning rate. The gradient represents the direction and degree of influence of each configuration parameter on the repair effect.

[0036] The visualization and interactive module is used to display information about contaminated sites, plant configuration schemes, real-time monitoring data, optimization suggestions, and remediation progress assessment results. It supports manual user intervention and parameter settings. It provides a web-based and mobile visualization platform that supports functions such as 3D model display of contaminated sites, real-time data dashboards, historical trend analysis, early warning alerts (such as alarms when soil moisture is below 20%), and report export. Users can manually adjust parameters through the interface, such as modifying the weight of remediation targets and setting monitoring frequencies.

[0037] The system, through the coordinated operation of the native plant resource database module, the contaminated site information collection and processing module, the plant community intelligent configuration module, the real-time monitoring and data feedback module, the dynamic optimization decision-making module, and the visualization interaction module, achieves full-cycle intelligent management from native plant selection, community construction, process monitoring to dynamic optimization, and is suitable for the ecological restoration of complex contaminated sites.

[0038] Optionally, the native plant resource database module also includes a plant image library, geographical distribution information, phenological data, and microbial interaction characteristic data, supporting multi-dimensional retrieval and matching based on pollution type, climate region, and soil conditions.

[0039] Optionally, the contaminated site information acquisition and processing module also integrates remote sensing technology and UAV aerial photography data for rapid surveying and 3D modeling of large-scale contaminated sites, generating high-precision pollution heat maps and ecological risk level zoning.

[0040] Optionally, the multi-objective optimization algorithm used by the intelligent configuration module of the plant community includes at least one of the non-dominated sorting genetic algorithm and the particle swarm optimization algorithm, and the optimization objectives also include biodiversity index, carbon sink function and landscape beautification value.

[0041] Optionally, the sensor network of the real-time monitoring and data feedback module includes a soil moisture sensor, a pH sensor, a heavy metal particle selective electrode, a polycyclic aromatic hydrocarbon fluorescence sensor, a weather station, and a plant growth imaging device. The data is integrated and preprocessed through an Internet of Things platform.

[0042] Optionally, the machine learning model used by the dynamic optimization decision module includes random forest, support vector machine or neural network model, to predict plant growth trends, pollutant migration patterns and remediation effects, and to generate adaptive adjustment strategies based on the prediction results.

[0043] Optionally, the system also includes a remediation effect evaluation module, which is used to periodically generate remediation progress reports, including pollutant removal rate, plant biomass changes, soil ecological function restoration indicators and economic cost analysis, to support the continuous improvement and verification of remediation solutions.

[0044] Optionally, the visual interactive interface supports access from both web and mobile devices, and provides functions such as map overlay display, data chart export, early warning prompts, and historical data backtracking.

[0045] Optionally, the system also supports data integration with an external environment management platform to enable the sharing of repair data and compliance reporting.

[0046] Optionally, the native plants include, but are not limited to, enriching plants, barrier plants, rhizosphere restoration plants, and plants that contribute to the long-term stability of the ecosystem.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] This invention achieves precise configuration and adaptive optimization of plant communities for the remediation of complex contaminated sites by constructing a system integrating a native plant resource database, intelligent collection of contaminated site information, multi-objective optimization algorithms, real-time monitoring and feedback, and dynamic decision-making. The system can scientifically select suitable native species, construct functionally synergistic plant communities, and dynamically adjust remediation strategies through continuous monitoring and machine learning. This significantly improves remediation efficiency, community stability, and ecological safety, overcoming the bottlenecks of traditional phytoremediation such as single species, blind configuration, and static schemes, and has significant environmental, economic, and social benefits. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of a native plant community configuration and dynamic optimization system for complex contaminated sites provided in an embodiment of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The purpose of this invention is to provide an intelligent system that can integrate a database of native plant resources, multi-objective optimization algorithms, real-time environmental monitoring technology, and dynamic decision-making models.

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Example 1:

[0055] Restoration project of a site left over from an electroplating plant:

[0056] The site is located in the suburbs of a city, covering an area of ​​approximately 30 mu (about 2 hectares). The main pollutants in the soil are cadmium (Cd), lead (Pb), and polycyclic aromatic hydrocarbons (PAHs), with an average Cd concentration of 2.5 mg / kg, an average Pb concentration of 450 mg / kg, and an average PAH concentration of 15 mg / kg. The soil pH is 6.8, the texture is sandy loam, and the average annual rainfall is 1200 mm.

[0057] Data acquisition and processing:

[0058] A preliminary survey was conducted using drones equipped with hyperspectral cameras and samplers. Fifty sampling points were set up for soil testing, and pollution distribution maps and environmental factor layers were generated by combining meteorological data.

[0059] Plant community configuration:

[0060] The system selects suitable native plants for the region from the database: *Pennisetum purpureus* (enriches Cd and Pb), *Alfalfa* (degrades PAHs in the rhizosphere), and *Phragmites australis* (blocks pollutant migration). A configuration scheme is generated using a multi-objective optimization algorithm.

[0061] Species composition: 50% Napier grass, 30% alfalfa, and 20% reeds.

[0062] Planting density: 10 plants / m² for Napier grass, 15 plants / m² for alfalfa, and 8 plants / m² for reeds.

[0063] Layout: Strip-shaped intermittent planting, with enriched plants located in the core pollution area.

[0064] Monitoring network layout:

[0065] Twenty sensor nodes were deployed, each containing soil moisture, pH, Cd / Pb electrodes, and PAHs sensors, and data was uploaded every four hours.

[0066] Dynamic optimization process:

[0067] After six months of operation, the system, using an LSTM model, predicted a Cd removal rate of only 15% in the northern region, lower than expected. The system automatically suggested:

[0068] The area was replanted with Napier grass, increasing the density to 12 plants / m².

[0069] Adjust the irrigation plan, adding irrigation once a week to promote plant growth. Managers confirm implementation via mobile devices.

[0070] Eighteen months later, the system generates an evaluation report:

[0071] The average removal rate of Cd was 42%, the removal rate of Pb was 38%, and the degradation rate of PAHs was 50%.

[0072] The plant community coverage reached 85%, and the Simpson index was 0.75.

[0073] The total cost per mu was 4,800 yuan, which was lower than the budget.

[0074] This system, through data-driven and intelligent decision-making, has achieved efficient, low-cost, green and sustainable remediation of this complexly contaminated site, verifying its feasibility and superiority in field applications.

[0075] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0076] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A system for configuring and dynamically optimizing native plant communities suitable for complex contaminated sites, characterized in that, The system comprises: a native plant resource database module for storing plant attribute data of various native plants, including plant species, growth characteristics, stress resistance, enrichment capacity, degradation capacity, root characteristics, suitable habitat conditions, and tolerance and repair efficiency data for heavy metals and organic pollutants; a contaminated site information collection and processing module for obtaining environmental data of the target contaminated site, including soil type, pH value, pollutant type, concentration and spatial distribution, water condition, climate data, and pre-processing and spatial interpolation analysis of the environmental data to generate pollution distribution map and environmental factor layer; a plant community intelligent configuration module based on the native plant resource database and the contaminated site information, using a multi-objective optimization algorithm to output an initial community configuration scheme containing various native plants, with repair efficiency, community stability, ecological safety and cost benefit as optimization objectives, the scheme including species composition, planting density, spatial layout and functional group structure; a real-time monitoring and data feedback module including a sensor network deployed in the contaminated site for continuous monitoring of plant growth status, soil physical and chemical properties, pollutant concentration changes and weather conditions, and sending monitoring data to the central processing unit through wireless transmission; a dynamic optimization decision module receiving the real-time monitoring data, combining the pre-set repair target and constraint conditions, using adaptive algorithm and machine learning model to dynamically adjust the plant community configuration, generating optimization instructions including species replacement suggestion, planting density adjustment, nutrient and water management strategy; a visual interaction module for displaying contaminated site information, plant configuration scheme, real-time monitoring data, optimization suggestion and repair progress evaluation results, supporting user manual intervention and parameter setting; The system realizes whole-cycle intelligent management from native plant screening, community construction, process monitoring to dynamic optimization through the collaborative operation of the native plant resource database module, the contaminated site information collection and processing module, the plant community intelligent configuration module, the real-time monitoring and data feedback module, the dynamic optimization decision module and the visual interaction module, and is suitable for ecological remediation of complex contaminated sites.

2. The system for configuring and dynamically optimizing a native plant community suitable for a complex contaminated site according to claim 1, wherein, The native plant resource database module further includes plant image library, geographical distribution information, phenology data and microbial interaction characteristic data, supporting multi-dimensional retrieval and matching according to pollution type, climate region and soil condition.

3. The system for configuring and dynamically optimizing a native plant community suitable for a complex contaminated site according to claim 1, wherein, The contaminated site information collection and processing module further integrates remote sensing technology and unmanned aerial vehicle aerial data for rapid investigation and three-dimensional modeling of large-scale contaminated sites, generating high-precision pollution heat map and ecological risk level zoning.

4. The system for configuring and dynamically optimizing a native plant community suitable for a complex contaminated site according to claim 1, wherein, The multi-objective optimization algorithm used by the plant community intelligent configuration module includes at least one of non-dominated sorting genetic algorithm and particle swarm optimization algorithm, and the optimization objectives further include biodiversity index, carbon sink function and landscape beautification value.

5. The system for configuring and dynamically optimizing a native plant community suitable for a complex contaminated site according to claim 1, wherein, The sensor network of the real-time monitoring and data feedback module includes soil moisture sensor, pH sensor, heavy metal particle selective electrode, polycyclic aromatic hydrocarbon fluorescence sensor, weather station and plant growth imaging equipment, and the data is integrated and pre-processed through the Internet of Things platform.

6. The system for configuring and dynamically optimizing a native plant community suitable for a complex contaminated site according to claim 1, wherein, The machine learning model adopted by the dynamic optimization decision module includes random forest, support vector machine or neural network model, which is used to predict plant growth trend, pollutant migration rule and remediation effect, and generate adaptive adjustment strategy based on the prediction result.

7. The system for configuring and dynamically optimizing a native plant community suitable for a complex contaminated site according to claim 1, wherein, The system also includes a remediation effect evaluation module for generating a remediation progress report regularly, including pollutant removal rate, plant biomass change, soil ecological function recovery index and economic cost analysis, supporting continuous improvement and verification of the remediation scheme.

8. The system for configuring and dynamically optimizing a native plant community suitable for a complex contaminated site according to claim 1, wherein, The visual interactive interface supports Web and mobile access, providing map overlay display, data chart export, early warning and historical data backtracking functions.

9. The system for configuring and dynamically optimizing a native plant community suitable for a complex contaminated site according to claim 1, wherein, The system also supports data docking with external environmental management platforms to realize sharing and compliance reporting of remediation data.

10. The system for configuring and dynamically optimizing a native plant community suitable for a complex contaminated site according to claim 1, wherein, The native plants include but are not limited to enrichment plants, barrier plants, rhizosphere remediation and long-term stability of the ecosystem.

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