Mangrove forest damaged ecosystem restoration and reconstruction system and method

Abstract

The invention belongs to the field of mangrove forest ecosystem restoration, and discloses a mangrove forest damaged ecosystem restoration and reconstruction system and method.The mangrove forest damaged ecosystem restoration and reconstruction system comprises an intelligent ecological matrix restoration module used for combination of a nanometer soil stabilizer and an organic modifier and dynamic regulation and control of matrix salinity; the nursery stock planting and dynamic monitoring module is used for performing real-time monitoring based on unmanned aerial vehicle remote sensing and an IoT sensor; the tide power regulation and control module is used for carrying out natural tide simulation and improving hydrodynamic conditions in combination with an artificial structure; and the biodiversity reconstruction module is used for co-construction of mangrove forest-fish and shrimp habitats. All the modules have a synergistic effect, dynamic management of the mangrove forest ecological system from matrix restoration to biodiversity reconstruction is achieved, and the restoration efficiency and long-term stability of the damaged mangrove forest ecological system are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the technical field of mangrove ecosystem restoration, and in particular relates to a system and method for restoring and rebuilding a damaged mangrove ecosystem. Background Art

[0002] Commonly used techniques for mangrove ecosystem restoration include artificial planting, tidal ecological regulation, and substrate improvement and stabilization. For example, artificial planting involves directly planting saline- and flood-resistant mangrove seedlings. However, this often results in low survival rates due to inappropriate site selection, insufficient tidal conditions, and deteriorating substrate environments. Furthermore, tidal ecological regulation attempts to restore the natural flooding rhythm of mangroves by dredging waterways or adjusting tidal frequency. However, this method is highly dependent on the overall environment and is costly.

[0003] In view of the above analysis, the technical problems that need to be solved urgently in the existing technology are:

[0004] (1) Low seedling survival rate: Planted mangrove seedlings are susceptible to tides, erosion, and saline-alkali stress, resulting in a survival rate of less than 50%;

[0005] (2) Deterioration of ecological matrix: Long-term pollution sedimentation and lack of hydrodynamics have led to salinization and nutrient imbalance in the mangrove matrix;

[0006] (3) Poor ecological connectivity: Mangrove areas are often disconnected from surrounding ecosystems, hindering biodiversity recovery;

[0007] (4) Insufficient intelligent monitoring and feedback mechanisms: There is a lack of efficient ecosystem monitoring technology and data-driven dynamic adjustment methods. Summary of the Invention

[0008] The present invention is achieved by providing a system for restoring and reconstructing a damaged mangrove ecosystem, characterized in that the system comprises:

[0009] Intelligent ecological matrix remediation module, including matrix sprayer, salt sensor, nutrient injection system, used for combining soil stabilizer and organic amendment, and dynamic control of matrix salt content;

[0010] The seedling planting and dynamic monitoring module includes vegetation growth monitoring sensors and multispectral drones for real-time monitoring based on drone remote sensing and IoT sensors;

[0011] Tidal power control module, including equipment such as tidal gates, dredging vessels, and hydrodynamic simulation systems, is used to simulate natural tides and improve hydrodynamic conditions by combining artificial structures;

[0012] The biodiversity reconstruction module includes equipment such as a habitat construction platform, aquatic biological monitoring devices, and a microbial injection system, which are used for the co-construction of mangrove-fish and shrimp habitats.

[0013] Furthermore, the signal processing step of the intelligent ecological matrix repair module includes:

[0014] (1) Data acquisition: the salt sensor monitors the salt concentration and distribution in the matrix in real time and outputs an electrical signal, which is converted into a digital signal by the analog-to-digital conversion module and transmitted to the control system;

[0015] (2) Signal processing and analysis: the control system filters the salt sensor data, removes noise signals, obtains the salt change curve, and calculates the dynamic control target of the matrix salt based on the obtained salt distribution data;

[0016] (3) Instruction generation and execution: the control system sends instructions to the matrix sprayer according to the set threshold of salt concentration. The matrix sprayer controls the dosage and range of the nano soil stabilizer and regulates the application ratio of the organic amendment through the nutrient injection system;

[0017] (4) Feedback signal monitoring: The salt data is collected again and compared with the target value. If the salt concentration exceeds the target range, the system adjusts the spraying and injection parameters to form a closed-loop dynamic control.

[0018] Furthermore, the signal processing step of the seedling planting and dynamic monitoring module includes:

[0019] (1) Data collection: vegetation growth monitoring sensors collect growth parameters (such as seedling height and crown width) and environmental parameters (such as temperature and humidity) of mangrove seedlings. Multispectral drones obtain vegetation coverage and reflectance spectrum information in the area to capture the vegetation health status of the mangrove area.

[0020] (2) Data fusion and preprocessing: the collected data of each sensor is transmitted to the central control system through the IoT gateway. The data is integrated with multi-source information through the data fusion algorithm to remove redundancy and noise;

[0021] (3) Dynamic analysis and decision-making: using multispectral images to classify vegetation health, using machine learning algorithms to assess whether seedling growth is normal, and based on environmental data fed back by sensors, the system generates instructions for adjusting planting density or replanting;

[0022] (4) Output and implementation: The mangrove seedling mechanical planter automatically completes the replanting of seedlings based on the generated planting density and positioning instructions, and the drone regularly conducts re-flight monitoring to form a closed loop of dynamic monitoring.

[0023] Furthermore, the signal processing step of the tidal power control module includes:

[0024] (1) Hydrodynamic data acquisition: The hydrodynamic simulation system obtains tidal flow data (such as water level, flow velocity, and flow direction) through water level sensors and flow velocity sensors, and transmits this data to the main control system in real time through telemetry equipment;

[0025] (2) Data simulation and modeling: input hydrodynamic data into hydrodynamic simulation software, establish a dynamic model of the tidal area through finite element analysis, systematically simulate the effects of different tidal control strategies, and output the optimal tidal control plan;

[0026] (3) Control command generation: Based on the simulation results, commands such as opening and closing frequency and duration are sent to the tidal gate. If the water power is insufficient, the control system activates the dredging vessel to remove silt and improve tidal conditions.

[0027] (4) Feedback and adjustment: The hydrodynamic data after regulation is collected in real time and compared with the model prediction value. If the deviation exceeds the set threshold, the system recalculates and adjusts the parameters of the tidal gate to form dynamic optimization control.

[0028] Furthermore, the biodiversity reconstruction module includes a signal processing step:

[0029] (1) Ecological monitoring data collection: aquatic biological monitoring devices capture the population size, distribution, and activity status of aquatic organisms such as fish and shrimp, and microbial sensors monitor the activity and quantity of microorganisms in the matrix in real time;

[0030] (2) Signal analysis and model building: the data processing unit classifies and clusters the data from the aquatic biological monitoring device and microbial sensor, establishes a biodiversity index model, and evaluates the ecological health level of the mangrove area;

[0031] (3) Ecological control instruction generation: the system sends instructions to the habitat construction platform based on the biodiversity index to optimize the habitat structure of fish and shrimp. If the microbial activity is lower than the target value, the system controls the microbial injection system to release symbiotic microorganisms to restore the microbial community;

[0032] (4) Dynamic feedback and optimization: regularly collect ecological monitoring data, evaluate the control effect, and the system optimizes ecological control parameters based on feedback data.

[0033] Another object of the present invention is to provide a method for restoring and rebuilding a damaged mangrove ecosystem, which specifically comprises:

[0034] S1: Intelligent ecological matrix repair

[0035] S11: Substrate survey and analysis, using salt sensors and soil sampling devices to obtain the salt distribution, pH and organic matter content of the regional matrix;

[0036] S12: Modifier spraying, evenly spraying nano soil stabilizer through matrix sprayer;

[0037] S13: Nutritional supplementation, injecting a specific proportion of organic amendments (such as humic acid) into the substrate;

[0038] S14: Real-time monitoring and feedback, dynamically monitoring soil salinity changes and adjusting salinity control thresholds in real time;

[0039] S2: Seedling planting and dynamic monitoring

[0040] S21: Selection of seedlings: Choose mangrove species that are suitable for local saline-alkali conditions (e.g. Kandelia candelata and Tung blossom trees);

[0041] S22: Automatic planting, using a mangrove seedling mechanical planter to plant at equal intervals and optimize planting density based on terrain characteristics;

[0042] S23: Intelligent monitoring: deploy vegetation growth sensors and use drones to regularly collect data such as vegetation coverage and seedling height to dynamically track growth status;

[0043] S24: Disaster early warning, based on real-time monitoring data, analyzes tidal erosion risks and deploys protective measures in advance;

[0044] S3: Tidal Power Regulation

[0045] S31: Hydrodynamic modeling, using a hydrodynamic simulation system to simulate tidal flows in mangrove areas and find the best hydrodynamic improvement solution;

[0046] S32: Artificial control, setting up tidal gates at key tidal breakpoints to control water flow and restore the natural flooding rhythm of mangroves;

[0047] S33: Dredging and Renovation, dredging areas with severe sedimentation;

[0048] S34: Dynamic adjustment, real-time monitoring of tidal water level changes, adjusting the frequency and duration of gate opening to maintain regional hydrodynamic balance;

[0049] S4: Biodiversity Restoration

[0050] S41: Habitat design: building habitat platforms for fish, shrimp and other aquatic organisms in mangrove areas;

[0051] S42: Microbial injection, using a microbial injection system to inject specific symbiotic microorganisms into the matrix;

[0052] S43: Enhance ecological connectivity by setting up pathways to enhance connectivity between mangrove areas and surrounding ecosystems;

[0053] S44: Long-term monitoring, using aquatic biological monitoring devices to assess changes in fish and shrimp populations and dynamically adjust habitat optimization plans.

[0054] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0055] 1) Improve seedling survival rate: Through the intelligent ecological matrix repair module, the stress of matrix salt on mangrove seedlings is reduced, while providing continuous nutrition supply, so that the seedling survival rate is increased to more than 80%;

[0056] 2) Restoring natural tidal ecological conditions: Utilizing a tidal power control module, the problem of insufficient tidal conditions in existing technologies is resolved, restoring the natural flooding rhythm of mangroves and enhancing the adaptive capacity of the ecosystem;

[0057] 3) Enhanced ecological diversity and system stability: The biodiversity restoration module not only increases the population density of aquatic organisms such as fish and shrimp, but also enhances the ecological service functions of mangroves through microbial technology, promoting the long-term stability of the ecosystem;

[0058] 4) Intelligence and dynamic adjustment: Based on drone remote sensing and IoT technology, the entire system implements intelligent monitoring and dynamic feedback, significantly reducing manual intervention and improving recovery efficiency and cost-effectiveness.

[0059] 5) Through the synergistic effects of four modules—intelligent ecological matrix restoration, seedling planting and dynamic monitoring, tidal dynamics control, and biodiversity restoration—this invention significantly improves the recovery efficiency and long-term stability of damaged mangrove ecosystems. Compared to existing technologies, this invention achieves comprehensive optimization from ecological matrix, tidal control, to biodiversity restoration, providing important technical support for the restoration of mangrove ecosystems worldwide. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a module diagram of a damaged mangrove ecosystem restoration and reconstruction system provided by an embodiment of the present invention;

[0061] Figure 2 This is a flow chart of a method for restoring and rebuilding a damaged mangrove ecosystem provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] like Figure 1As shown, an embodiment of the present invention provides a system for restoring and reconstructing a damaged mangrove ecosystem, comprising:

[0064] Intelligent ecological matrix remediation module, including matrix sprayer, salt sensor, nutrient injection system, for combining nano-soil stabilizer with organic amendment and dynamic control of matrix salt content;

[0065] The seedling planting and dynamic monitoring module includes a mangrove seedling mechanical planter, vegetation growth monitoring sensors, and a multispectral drone for real-time monitoring based on drone remote sensing and IoT sensors.

[0066] Tidal power control module, including equipment such as tidal gates, dredging vessels, and hydrodynamic simulation systems, is used to simulate natural tides and improve hydrodynamic conditions by combining artificial structures;

[0067] The biodiversity reconstruction module includes equipment such as a habitat construction platform, aquatic biological monitoring devices, and a microbial injection system, which are used for the co-construction of mangrove-fish and shrimp habitats.

[0068] This module aims to create a highly stable ecological matrix suitable for mangrove growth by introducing a combination of nano-soil stabilizers and organic amendments. Nano-soil stabilizers, through their high specific surface area and chemical activity, form a stable network structure with soil particles, thereby enhancing the soil's resistance to erosion and water retention. At the same time, organic amendments (such as seaweed extracts and humic acid) can significantly improve the organic matter content and nutrient availability of the matrix. To achieve precise control of matrix conditions, this module is equipped with a salt sensor network and a nutrient injection system. The salt sensor monitors the salt concentration of the matrix in real time using conductivity and ion-selective electrode methods, and is coupled with an automated nutrient injection system to ensure that the matrix's conductivity remains within the optimal range of 3-6dS / m. Through intelligent algorithm optimization and dynamic regulation, this module can provide a suitable growth environment under different tidal cycles and seasonal changes.

[0069] The core task of this module is to achieve efficient planting of mangrove seedlings and full life cycle monitoring of their growth status. By combining mechanized planting equipment (such as mangrove seedling mechanical planters) with manual replanting, the uniformity and accuracy of large-scale planting can be ensured. Multispectral drones are used in conjunction with vegetation growth monitoring sensors (NDVI, EVI, PRI, etc.) to collect remote sensing data to achieve real-time monitoring of vegetation coverage, photosynthetic efficiency, and biomass accumulation. The multispectral sensors carried by drones can identify the health status and growth potential of seedlings through changes in reflectance in specific bands. In addition, the linkage between the vegetation monitoring system and the IoT network can provide more refined data support, enabling accurate analysis and prediction of seedling growth in both spatial and temporal dimensions.

[0070] Tidal dynamics is one of the key ecological factors affecting the growth and expansion of mangroves. This module simulates and optimizes the hydrological characteristics of natural tides by constructing engineering facilities such as tidal gates and dredging vessels, combined with a hydrodynamic simulation system. The opening and closing control of tidal gates is based on a comprehensive analysis of sea level, tidal range, tidal velocity, and sediment movement. By combining CFD (computational fluid dynamics) simulation with hydrodynamic models such as MIKE 21, this module can regulate the intensity and frequency of tidal flows in real time to ensure the uniformity of sediment transport and deposition. In damaged mangrove areas, dredging vessels are used to carry out targeted removal or replenishment of sediment accumulation areas to enhance the efficiency of tidal exchange and water quality renewal, thereby improving the hydrodynamic conditions and nutrient availability of mangrove habitats.

[0071] This module is dedicated to rebuilding the diversity and improving the stability of the ecosystem by constructing a mangrove-fish and shrimp habitat co-construction system. The habitat construction platform includes artificial reefs, floating habitats and underwater shelters, providing suitable habitats for a variety of aquatic organisms. At the same time, aquatic biological monitoring devices (such as sonar detectors and water quality sensors) are used to monitor the population dynamics, behavioral patterns and changes in the habitat of aquatic organisms in real time. In order to promote the recovery and stability of biodiversity, this module also introduces a microbial injection system to improve the bottom structure and nutrient cycle efficiency by adding specific symbiotic microorganisms and nitrogen-fixing bacteria. Experiments have shown that this method can significantly improve the species richness and community diversity of benthic animals, and enhance the stability and resistance of the ecosystem.

[0072] In order to achieve intelligent management of the mangrove ecological restoration process, the present invention constructs a monitoring and feedback control system based on the Internet of Things (IoT) and artificial intelligence (AI) technologies. All sensor nodes transmit data through wireless networks (such as LoRa or NB-IoT), and realize real-time processing and decision-making through edge computing modules. The system uses deep learning algorithms (such as convolutional neural networks (CNN) and recurrent neural networks (RNN)) to predict and optimize vegetation growth status, tidal dynamics and biodiversity. The feedback control module dynamically adjusts matrix restoration, tidal regulation and biodiversity construction according to the data analysis results at different time scales, thereby achieving refined management of the ecosystem restoration process.

[0073] In the pilot areas along the coast of Hainan and Guangdong, the mangrove ecological restoration system provided by the present invention has been successfully applied to multiple damaged areas. Through two years of dynamic monitoring and evaluation, the vegetation coverage rate increased by 35.4%, and the net primary productivity (NPP) of plants increased by 28.7%. Biodiversity indicators improved significantly, among which the density of fish and shrimp populations increased by 42.6%. Through the application of intelligent monitoring and feedback control systems, the efficiency and sustainability of ecological restoration have been significantly improved. At the same time, the modular design of the system gives it good adaptability and scalability, providing a new technical path for the restoration and protection of coastal wetland ecosystems.

[0074] In the embodiment of the present invention, a mangrove seedling mechanical planter is used for large-scale planting. The vegetation coverage data of the mangrove area is obtained based on a multispectral drone, and the growth is monitored by a vegetation index (such as NDVI). The NDVI model is as follows:

[0075]

[0076] RED stands for near-infrared and red reflectance, respectively. Using vegetation growth monitoring sensors and drone data, we dynamically adjust planting density and replanting areas to ensure uniform vegetation restoration.

[0077] In the embodiment of the present invention, the tidal power control module simulates natural tides and is optimized in combination with the hydrodynamic simulation system. The two-dimensional shallow water equation model is used to calculate the water flow dynamics:

[0078]

[0079] Where h is the water depth, u is the velocity vector, g is the acceleration due to gravity, and F is the external force (such as the effect of tidal gates). Adjusting gate opening and closing times and dredging vessel operations optimizes hydrodynamic conditions and ensures suitable water flow at the mangrove roots.

[0080] The signal processing step of the intelligent ecological matrix repair module includes:

[0081] (1) Data acquisition: the salt sensor monitors the salt concentration and distribution in the matrix in real time and outputs an electrical signal, which is converted into a digital signal by the analog-to-digital conversion module and transmitted to the control system;

[0082] (2) Signal processing and analysis: the control system filters the salt sensor data, removes noise signals, obtains the salt change curve, and calculates the dynamic control target of the matrix salt based on the obtained salt distribution data;

[0083] (3) Instruction generation and execution: the control system sends instructions to the matrix sprayer according to the set threshold of salt concentration. The matrix sprayer controls the dosage and range of the nano soil stabilizer and regulates the application ratio of the organic amendment through the nutrient injection system;

[0084] (4) Feedback signal monitoring: The salt data is collected again and compared with the target value. If the salt concentration exceeds the target range, the system adjusts the spraying and injection parameters to form a closed-loop dynamic control.

[0085] The signal processing step of the seedling planting and dynamic monitoring module includes:

[0086] (1) Data collection: vegetation growth monitoring sensors collect growth parameters (such as seedling height and crown width) and environmental parameters (such as temperature and humidity) of mangrove seedlings. Multispectral drones obtain vegetation coverage and reflectance spectrum information in the area to capture the vegetation health status of the mangrove area.

[0087] (2) Data fusion and preprocessing: the collected data of each sensor is transmitted to the central control system through the IoT gateway. The data is integrated with multi-source information through the data fusion algorithm to remove redundancy and noise;

[0088] (3) Dynamic analysis and decision-making: using multispectral images to classify vegetation health, using machine learning algorithms to assess whether seedling growth is normal, and based on environmental data fed back by sensors, the system generates instructions for adjusting planting density or replanting;

[0089] (4) Output and implementation: The mangrove seedling mechanical planter automatically completes the replanting of seedlings based on the generated planting density and positioning instructions, and the drone regularly conducts re-flight monitoring to form a closed loop of dynamic monitoring.

[0090] The signal processing step of the tidal power control module includes:

[0091] (1) Hydrodynamic data acquisition: The hydrodynamic simulation system obtains tidal flow data (such as water level, flow velocity, and flow direction) through water level sensors and flow velocity sensors, and transmits this data to the main control system in real time through telemetry equipment;

[0092] (2) Data simulation and modeling: input hydrodynamic data into hydrodynamic simulation software, establish a dynamic model of the tidal area through finite element analysis, systematically simulate the effects of different tidal control strategies, and output the optimal tidal control plan;

[0093] (3) Control command generation: Based on the simulation results, commands such as opening and closing frequency and duration are sent to the tidal gate. If the water power is insufficient, the control system activates the dredging vessel to remove silt and improve tidal conditions.

[0094] (4) Feedback and adjustment: The hydrodynamic data after regulation is collected in real time and compared with the model prediction value. If the deviation exceeds the set threshold, the system recalculates and adjusts the parameters of the tidal gate to form dynamic optimization control.

[0095] The biodiversity reconstruction module, the signal processing step includes:

[0096] (1) Ecological monitoring data collection: aquatic biological monitoring devices capture the population size, distribution, and activity status of aquatic organisms such as fish and shrimp, and microbial sensors monitor the activity and quantity of microorganisms in the matrix in real time;

[0097] (2) Signal analysis and model building: the data processing unit classifies and clusters the data from the aquatic biological monitoring device and microbial sensor, establishes a biodiversity index model, and evaluates the ecological health level of the mangrove area;

[0098] (3) Ecological control instruction generation: the system sends instructions to the habitat construction platform based on the biodiversity index to optimize the habitat structure of fish and shrimp. If the microbial activity is lower than the target value, the system controls the microbial injection system to release symbiotic microorganisms to restore the microbial community;

[0099] (4) Dynamic feedback and optimization: regularly collect ecological monitoring data, evaluate the control effects, and systematically optimize ecological control parameters based on feedback data to ensure continuous improvement of biodiversity.

[0100] Through the signal processing described above, the modules work synergistically to achieve dynamic management of mangrove ecosystems, from matrix restoration to biodiversity reconstruction. This embodiment of the present invention not only efficiently collects and processes multi-source ecological data, but also achieves comprehensive restoration of mangrove ecological functions through closed-loop control and dynamic optimization.

[0101] like Figure 2 As shown, an embodiment of the present invention provides a method for restoring and rebuilding a damaged mangrove ecosystem, specifically comprising:

[0102] S1: Intelligent ecological matrix repair

[0103] S11: Substrate survey and analysis, using salt sensors and soil sampling devices to obtain the salt distribution, pH and organic matter content of the regional matrix;

[0104] S12: Modifier spraying, using a matrix sprayer to evenly spray nano soil stabilizer to improve the structural stability of the matrix and reduce the salt volatilization rate;

[0105] S13: Nutritional supplementation: injecting a specific proportion of organic amendments (such as humic acid) into the substrate to increase the nutrient content and enhance the root development ability of mangrove seedlings;

[0106] S14: Real-time monitoring and feedback, dynamically monitoring soil salinity changes, adjusting the salt control threshold in real time to avoid secondary soil salinization;

[0107] S2: Seedling planting and dynamic monitoring

[0108] S21: For seedling selection, choose mangrove species that are suitable for local saline-alkali conditions (such as Kandelia candel and Tung blossom trees) to ensure resistance;

[0109] S22: Automatic planting, using a mangrove seedling mechanical planter to plant at equal intervals and optimize planting density based on terrain characteristics;

[0110] S23: Intelligent monitoring: deploy vegetation growth sensors and use drones to regularly collect data such as vegetation coverage and seedling height to dynamically track growth status;

[0111] S24: Disaster warning: Based on real-time monitoring data, analyze tidal erosion risks and deploy protective measures in advance.

[0112] S3: Tidal Power Regulation

[0113] S31: Hydrodynamic modeling, using a hydrodynamic simulation system to simulate tidal flows in mangrove areas and find the best hydrodynamic improvement solution;

[0114] S32: Artificial control, setting up tidal gates at key tidal breakpoints to control water flow and restore the natural flooding rhythm of mangroves;

[0115] S33: Dredging and transformation: dredging areas with severe sedimentation to improve hydrodynamic conditions and mitigate the adverse effects of sedimentation on seedling survival;

[0116] S34: Dynamic adjustment, real-time monitoring of tidal water level changes, adjusting the frequency and duration of gate opening to maintain regional hydrodynamic balance;

[0117] S4: Biodiversity Restoration

[0118] S41: Habitat design: building habitats for aquatic organisms such as fish and shrimp in mangrove areas to enhance ecosystem diversity;

[0119] S42: Microbial injection, using a microbial injection system to inject specific symbiotic microorganisms into the substrate to enhance the nitrogen fixation capacity of mangrove roots;

[0120] S43: Enhance ecological connectivity by establishing pathways to enhance connectivity between mangrove areas and surrounding ecosystems, facilitating species migration and resource flows;

[0121] S44: Long-term monitoring, using aquatic biological monitoring devices to assess changes in fish and shrimp populations and dynamically adjust habitat optimization plans.

[0122] Example 1: Restoration of mangrove ecosystem after typhoon

[0123] In a coastal area, typhoons caused extensive damage to mangroves, increasing surface salt concentrations, disrupting soil structure, worsening the growing environment for mangrove seedlings, and significantly reducing biodiversity. Traditional restoration methods, lacking dynamic monitoring and precise control, have been ineffective.

[0124] (1) Intelligent ecological matrix restoration: Nano-soil stabilizers and organic amendments are evenly sprayed on the damaged area using a matrix sprayer. A salt sensor monitors the soil salt concentration in real time. Based on sensor feedback, the system dynamically adjusts the nutrient injection ratio to ensure that the matrix salt content is reduced to an appropriate range.

[0125] (2) Seedling Planting and Dynamic Monitoring: Mangrove seedlings are planted at a set density in the repaired substrate using a mangrove seedling mechanical planter. Drones monitor the coverage and health of the mangrove seedlings, and IoT sensors collect real-time growth data.

[0126] (3) Tidal power control: The system monitors tidal dynamics through water level sensors and uses tidal gates to control hydrodynamic conditions to prevent excessive siltation or excessive salinity in the substrate. In areas with insufficient hydrodynamics, dredging vessels are activated to optimize water flow channels and improve the growth environment of mangrove seedlings.

[0127] (4) Biodiversity restoration: Use a microbial injection system to release specific symbiotic microorganisms to promote ecological circulation within the matrix. Install habitat construction platforms in damaged areas to provide shelter for aquatic organisms such as fish and shrimp, gradually restoring biodiversity.

[0128] The soil salinity concentration was reduced to a suitable level for mangroves.

[0129] The survival rate of mangrove seedlings has increased to 90%, and the vegetation coverage rate has increased significantly.

[0130] The number of fish and shrimp populations has recovered to pre-disaster levels, and the biodiversity index has increased by 40%.

[0131] Example 2: Treatment of mangrove pollution after industrial wastewater discharge

[0132] Due to long-term wastewater discharge in a certain industrial area, the mangroves have been polluted, the water dynamics have been disrupted, a large number of mangrove seedlings have died, the habitats of fish and shrimp have been destroyed, and the ecological functions have seriously degraded.

[0133] (1) Intelligent ecological matrix remediation: Use salt sensors and pollutant monitoring equipment to conduct comprehensive matrix testing. Nano-soil stabilizers and organic amendments are applied through matrix sprayers, while nutrient solution is dynamically injected to decompose and adsorb harmful substances in the matrix.

[0134] (2) Planting and dynamic monitoring of seedlings: Mechanical planters plant mangrove seedlings that are highly resistant to pollution. Drones and IoT sensors collaborate to monitor vegetation health, record seedling growth data, and identify areas of abnormal growth caused by pollution.

[0135] (3) Tidal power control: Install tidal gates to simulate natural tidal flows and remove pollutants. Use a hydrodynamic simulation system to optimize water flow patterns and reduce the deposition of industrial pollutants in mangrove areas.

[0136] (4) Biodiversity restoration: Inject specific microbial flora to enhance pollutant degradation efficiency. Habitat construction platforms restore the habitat for fish and shrimp, gradually attracting the return of aquatic populations.

[0137] The pollutant content in mangrove matrix has been reduced to below environmental protection standards.

[0138] The survival rate of mangrove seedlings reached over 85%, and the vegetation coverage in the polluted areas recovered to 75% of the original level.

[0139] The aquatic ecosystem has gradually recovered, the number of fish and shrimp populations has increased, and the ecological function has been significantly improved.

[0140] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A system for restoring and reconstructing damaged mangrove ecosystems, characterized in that: The system includes: Intelligent ecological matrix remediation module, including matrix sprayer, salt sensor, nutrient injection system, for combining nano-soil stabilizer with organic amendment and dynamic control of matrix salt content; The seedling planting and dynamic monitoring module includes a mangrove seedling mechanical planter, vegetation growth monitoring sensors, and a multispectral drone for real-time monitoring based on drone remote sensing and IoT sensors. Tidal power control module, including equipment such as tidal gates, dredging vessels, and hydrodynamic simulation systems, is used to simulate natural tides and improve hydrodynamic conditions by combining artificial structures; The biodiversity reconstruction module includes equipment such as a habitat construction platform, aquatic biological monitoring devices, and a microbial injection system, which are used for the co-construction of mangrove-fish and shrimp habitats.

2. The damaged mangrove ecosystem restoration and reconstruction system according to claim 1, characterized in that: The signal processing step of the intelligent ecological matrix repair module includes: (1) Data acquisition: the salt sensor monitors the salt concentration and distribution in the matrix in real time and outputs an electrical signal, which is converted into a digital signal by the analog-to-digital conversion module and transmitted to the control system; (2) Signal processing and analysis: the control system filters the salt sensor data, removes noise signals, obtains the salt change curve, and calculates the dynamic control target of the matrix salt based on the obtained salt distribution data; (3) Instruction generation and execution: the control system sends instructions to the matrix sprayer according to the set threshold of salt concentration. The matrix sprayer controls the dosage and range of the nano soil stabilizer and regulates the application ratio of the organic amendment through the nutrient injection system; (4) Feedback signal monitoring: The salt data is collected again and compared with the target value. If the salt concentration exceeds the target range, the system adjusts the spraying and injection parameters to form a closed-loop dynamic control.

3. The damaged mangrove ecosystem restoration and reconstruction system according to claim 1, characterized in that: The signal processing step of the seedling planting and dynamic monitoring module includes: (1) Data collection: vegetation growth monitoring sensors collect growth parameters and environmental parameters of mangrove seedlings, and multispectral drones obtain vegetation coverage and reflectance spectrum information in the area to capture the vegetation health status of the mangrove area; (2) Data fusion and preprocessing: the collected data of each sensor is transmitted to the central control system through the IoT gateway. The data is integrated with multi-source information through the data fusion algorithm to remove redundancy and noise; (3) Dynamic analysis and decision-making: using multispectral images to classify vegetation health, using machine learning algorithms to assess whether seedling growth is normal, and based on environmental data fed back by sensors, the system generates instructions for adjusting planting density or replanting; (4) Output and implementation: The mangrove seedling mechanical planter automatically completes the replanting of seedlings based on the generated planting density and positioning instructions, and the drone regularly conducts re-flight monitoring to form a closed loop of dynamic monitoring.

4. The damaged mangrove ecosystem restoration and reconstruction system according to claim 1, characterized in that: The signal processing step of the tidal power control module includes: (1) Hydrodynamic data acquisition: The hydrodynamic simulation system obtains tidal flow data through water level sensors and flow velocity sensors, and transmits this data to the main control system in real time through telemetry equipment; (2) Data simulation and modeling: input hydrodynamic data into hydrodynamic simulation software, establish a dynamic model of the tidal area through finite element analysis, systematically simulate the effects of different tidal control strategies, and output the optimal tidal control plan; (3) Control command generation: Based on the simulation results, commands such as opening and closing frequency and duration are sent to the tidal gate. If the water power is insufficient, the control system activates the dredging vessel to remove silt and improve tidal conditions. (4) Feedback and adjustment: The hydrodynamic data after regulation is collected in real time and compared with the model prediction value. If the deviation exceeds the set threshold, the system recalculates and adjusts the parameters of the tidal gate to form dynamic optimization control.

5. The damaged mangrove ecosystem restoration and reconstruction system according to claim 1, characterized in that: The biodiversity reconstruction module, the signal processing step includes: (1) Ecological monitoring data collection: aquatic biological monitoring devices capture the population size, distribution, and activity status of aquatic organisms such as fish and shrimp, and microbial sensors monitor the activity and quantity of microorganisms in the matrix in real time; (2) Signal analysis and model building: the data processing unit classifies and clusters the data from the aquatic biological monitoring device and microbial sensor, establishes a biodiversity index model, and evaluates the ecological health level of the mangrove area; (3) Ecological control instruction generation: the system sends instructions to the habitat construction platform based on the biodiversity index to optimize the habitat structure of fish and shrimp. If the microbial activity is lower than the target value, the system controls the microbial injection system to release symbiotic microorganisms to restore the microbial community; (4) Dynamic feedback and optimization: regularly collect ecological monitoring data, evaluate the control effect, and the system optimizes ecological control parameters based on feedback data.

6. A method for restoring and reconstructing a damaged mangrove ecosystem according to claims 1-5, characterized in that: The method specifically includes: S1: Intelligent ecological matrix repair S11: Substrate survey and analysis, using salt sensors and soil sampling devices to obtain the salt distribution, pH and organic matter content of the regional matrix; S12: Modifier spraying, evenly spraying nano soil stabilizer through matrix sprayer; S13: Nutritional supplementation, injecting a specific proportion of organic amendments into the substrate; S14: Real-time monitoring and feedback, dynamically monitoring soil salinity changes and adjusting salinity control thresholds in real time; S2: Seedling planting and dynamic monitoring S21: Seedling selection: choose mangrove species that are suitable for local saline-alkali conditions; S22: Automatic planting, using a mangrove seedling mechanical planter to plant at equal intervals and optimize planting density based on terrain characteristics; S23: Intelligent monitoring: deploy vegetation growth sensors and use drones to regularly collect data such as vegetation coverage and seedling height to dynamically track growth status; S24: Disaster early warning, based on real-time monitoring data, analyzes tidal erosion risks and deploys protective measures in advance; S3: Tidal Power Regulation S31: Hydrodynamic modeling, using a hydrodynamic simulation system to simulate tidal flows in mangrove areas and find the best hydrodynamic improvement solution; S32: Artificial control, setting up tidal gates at key tidal breakpoints to control water flow and restore the natural flooding rhythm of mangroves; S33: Dredging and Renovation, dredging areas with severe sedimentation; S34: Dynamic adjustment, real-time monitoring of tidal water level changes, adjusting the frequency and duration of gate opening to maintain regional hydrodynamic balance; S4: Biodiversity Restoration S41: Habitat design: building habitat platforms for fish, shrimp and other aquatic organisms in mangrove areas; S42: Microbial injection, using a microbial injection system to inject specific symbiotic microorganisms into the matrix; S43: Enhance ecological connectivity by setting up pathways to enhance connectivity between mangrove areas and surrounding ecosystems; S44: Long-term monitoring, using aquatic biological monitoring devices to assess changes in fish and shrimp populations and dynamically adjust habitat optimization plans.

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