A mangrove damaged ecosystem restoration and reconstruction system and method

Abstract

The present application belongs to the field of mangrove ecosystem restoration, and discloses a mangrove damaged ecosystem restoration and reconstruction system and method, comprising: an intelligent ecological substrate repair module for combining nano soil stabilizer and organic modifier, and dynamically regulating substrate salt content; a seedling planting and dynamic monitoring module for real-time monitoring based on unmanned aerial vehicle remote sensing and IoT sensors; a tidal power regulation module for simulating natural tides and improving hydrodynamic conditions by combining artificial structures; and a biodiversity reconstruction module for co-constructing mangrove-fish and shrimp habitats. The modules work together to achieve dynamic management of mangrove ecosystems from substrate repair to biodiversity reconstruction, significantly improving the restoration efficiency and long-term stability of damaged mangrove ecosystems.

Description

Technical Field

[0001] This invention belongs to, but is not limited to, the field of mangrove ecosystem restoration technology, and particularly relates to a system and method for the restoration and reconstruction of damaged mangrove ecosystems. Background Technology

[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 salt-tolerant and flood-resistant mangrove seedlings, but survival rates are often low due to improper site selection, insufficient tidal conditions, and deteriorating substrate environments. Furthermore, tidal ecological regulation techniques attempt to restore the natural flooding rhythm of mangroves by dredging waterways or adjusting tidal frequencies, but this method is highly dependent on the overall environment and is costly.

[0003] Based on the above analysis, the urgent technical problems that need to be solved in the existing technology are: (1) Low seedling survival rate: The planted mangrove seedlings are susceptible to the effects of tides, erosion and saline-alkali stress, resulting in a survival rate of less than 50%; (2) Deterioration of ecological matrix: Long-term pollution deposition and lack of hydrodynamics have led to the salinization and nutrient imbalance of the matrix in mangrove areas; (3) Poor ecological connectivity: Mangrove areas are often disconnected from the surrounding ecosystems, which hinders the recovery of biodiversity; (4) Insufficient intelligent monitoring and feedback mechanisms: lack of efficient ecosystem monitoring technologies and data-driven dynamic adjustment methods. Summary of the Invention

[0004] This invention is implemented as follows: a system for the restoration and reconstruction of damaged mangrove ecosystems, characterized in that the system comprises: The intelligent ecological matrix remediation module includes a matrix sprayer, a salinity sensor, and a nutrient injection system, which is used for the combination of soil stabilizers and organic amendments and for dynamic regulation of matrix salinity. The seedling planting and dynamic monitoring module includes equipment such as vegetation growth monitoring sensors and multispectral drones, which are used for real-time monitoring based on drone remote sensing and IoT sensors. The tidal dynamic control module includes equipment such as tidal gates, dredging vessels, and hydrodynamic simulation systems, which are used to simulate natural tides and improve hydrodynamic conditions in conjunction with artificial structures. The biodiversity reconstruction module includes a habitat construction platform, aquatic life monitoring devices, and a microbial injection system, which are used for the co-construction of mangrove-fish and shrimp habitats.

[0005] Furthermore, the signal processing steps of the intelligent ecological matrix restoration module include: (1) Data acquisition: The salt sensor monitors the salt concentration and distribution in the matrix in real time and outputs an electrical signal. The signal is converted into a digital signal by the analog-to-digital converter and transmitted to the control system. (2) Signal processing and analysis: The control system filters the salinity sensor data, removes noise signals, obtains the salinity change curve, and calculates the dynamic control target of matrix salinity based on the obtained salinity distribution data. (3) Command generation and execution: The control system sends commands to the substrate sprayer according to the set threshold of salt concentration. The substrate sprayer controls the dosage and range of the nano soil stabilizer and regulates the application ratio of organic amendment through the nutrient injection system. (4) Feedback signal monitoring: Collect salt data again and compare it 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.

[0006] Furthermore, the signal processing steps of the seedling planting and dynamic monitoring module include: (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, and multispectral drones acquire 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 data collected by each sensor is transmitted to the central control system through the IoT gateway. The data is integrated from multiple sources through the data fusion algorithm to remove redundancy and noise. (3) Dynamic analysis and decision-making: use multispectral images to classify vegetation health, use machine learning algorithms to assess whether seedling growth is normal, and generate instructions to adjust planting density or replant based on environmental data fed back by sensors. (4) Output and implementation: The mangrove seedling mechanical planter automatically completes the replanting of seedlings according to the generated planting density and positioning instructions, and the drone regularly re-flies to monitor, forming a closed loop of dynamic monitoring.

[0007] Furthermore, the tidal dynamic control module includes the following signal processing steps: (1) Hydrodynamic data acquisition: The hydrodynamic simulation system acquires tidal flow data (such as water level height, flow velocity, and flow direction) through water level sensors and flow velocity sensors, and transmits these data to the main control system in real time through telemetry equipment; (2) Data simulation and modeling: hydrodynamic data is input into hydrodynamic simulation software, and a dynamic model of the tidal region is established through finite element analysis. The system simulates the effects of different tidal regulation strategies and outputs the best tidal regulation scheme. (3) Control command generation: Based on the simulation results, send commands such as opening and closing frequency and duration to the tidal gate. If the hydrodynamic force is insufficient, the control system starts the dredging vessel to remove silt and improve the tidal conditions. (4) Feedback and adjustment: Real-time collection of hydrodynamic data after regulation and comparison with model prediction values. If the deviation exceeds the set threshold, the system recalculates and adjusts the parameters of the tidal gate to form dynamic optimization control.

[0008] Furthermore, the signal processing steps of the biodiversity reconstruction module include: (1) Ecological monitoring data collection: Aquatic organism 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 substrate in real time. (2) Signal analysis and model building: The data processing unit classifies and clusters the data from the aquatic organism monitoring device and microbial sensor, establishes a biodiversity index model, and assesses the ecological health level of the mangrove area; (3) Ecological regulation command generation: The system sends commands to the habitat construction platform based on the biodiversity index to optimize the structure of fish and shrimp habitats. 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: collect ecological monitoring data regularly, evaluate the regulation effect, and optimize ecological regulation parameters based on feedback data.

[0009] Another objective of this invention is to provide a method for the restoration and reconstruction of damaged mangrove ecosystems, specifically including: S1: Intelligent Ecological Matrix Restoration S11: Matrix survey and analysis, using salinity sensors and soil sampling devices to obtain the salinity distribution, pH and organic matter content of the regional matrix; S12: Soil conditioner spraying, nano soil stabilizer is evenly sprayed through a substrate spraying machine; S13: Nutritional supplementation, injecting a specific proportion of organic modifiers (such as humic acid) into the matrix. S14: Real-time monitoring and feedback, dynamically monitoring changes in soil salinity, and adjusting salinity control thresholds in real time; S2: Seedling Planting and Dynamic Monitoring S21: Seedling selection: Select mangrove plant species suitable for local saline-alkali conditions (such as Kandelia candel and Paulownia tomentosa). S22: Automatic planting, using a mangrove seedling mechanical planting machine to plant at equal intervals, and optimizing the planting density according to the terrain characteristics; S23: Intelligent monitoring, deploying vegetation growth sensors, and using 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 the risk of tidal erosion and deploys protective measures in advance; S3: Tidal Dynamic Regulation S31: Hydrodynamic modeling, using a hydrodynamic simulation system to simulate tidal flow in mangrove areas and find the best hydrodynamic improvement scheme; S32: Artificial regulation involves setting up tidal gates at key tidal break points to control water flow and restore the natural flooding rhythm of mangroves; S33: Dredging and modification, dredging areas with severe sedimentation; S34: Dynamic adjustment, real-time monitoring of tidal water level changes, adjustment of gate opening frequency and duration to maintain regional hydrodynamic balance; S4: Biodiversity Restoration S41: Habitat design, creating habitat platforms for aquatic organisms such as fish and shrimp in mangrove areas; S42: Microbial injection, using a microbial injection system to inject specific symbiotic microorganisms into the matrix; S43: Enhanced ecological connectivity by establishing pathways to improve connectivity between mangrove areas and surrounding ecosystems; S44: Long-term monitoring, using aquatic organism monitoring devices to assess changes in fish and shrimp populations, and dynamically adjusting habitat optimization plans.

[0010] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows: 1) Improve seedling survival rate: Through the intelligent ecological substrate repair module, the stress effect of substrate salt on mangrove seedlings is reduced, while providing continuous nutrient supply, thereby increasing the seedling survival rate to over 80%. 2) Restoring natural tidal ecological conditions: By utilizing the tidal dynamic regulation module, the problem of insufficient tidal conditions in existing technologies has been solved, the natural flooding rhythm of mangroves has been restored, and the adaptive capacity of the ecosystem has been enhanced. 3) Enhance ecological diversity and system stability: The biodiversity reconstruction 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; 4) Intelligent and dynamic adjustment: Based on UAV remote sensing and IoT technology, the entire system realizes intelligent monitoring and dynamic feedback, which greatly reduces manual intervention and improves recovery efficiency and cost-effectiveness.

[0011] 5) Through the synergistic effect of four modules—intelligent ecological substrate restoration, seedling planting and dynamic monitoring, tidal dynamic regulation, and biodiversity reconstruction—this invention significantly improves the restoration efficiency and long-term stability of damaged mangrove ecosystems. Compared with existing technologies, this invention achieves comprehensive optimization from ecological substrate and tidal regulation to biodiversity reconstruction, providing important technical support for the restoration of global mangrove ecosystems. Attached Figure Description

[0012] Figure 1 This is a block diagram of a mangrove damaged ecosystem restoration and reconstruction system provided in an embodiment of the present invention; Figure 2 This is a flowchart of the method for restoring and rebuilding damaged mangrove ecosystems provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0014] like Figure 1 As shown, this embodiment of the invention provides a system for the restoration and reconstruction of damaged mangrove ecosystems, including: The intelligent ecological matrix remediation module includes a matrix sprayer, a salinity sensor, and a nutrient injection system, which is used to combine nano-soil stabilizers with organic amendments and to dynamically regulate matrix salinity. The seedling planting and dynamic monitoring module includes equipment such as a mangrove seedling mechanical planting machine, vegetation growth monitoring sensors, and a multispectral drone, which is used for real-time monitoring based on drone remote sensing and IoT sensors. The tidal dynamic control module includes equipment such as tidal gates, dredging vessels, and hydrodynamic simulation systems, which are used to simulate natural tides and improve hydrodynamic conditions in conjunction with artificial structures. The biodiversity reconstruction module includes a habitat construction platform, aquatic life monitoring devices, and a microbial injection system, which are used for the co-construction of mangrove-fish and shrimp habitats.

[0015] This module aims to construct a highly stable ecological substrate suitable for mangrove growth by introducing the combined application of nano-soil stabilizers and organic amendments. Nano-soil stabilizers, with 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. Simultaneously, organic amendments (such as seaweed extract and humic acid) significantly improve the substrate's organic matter content and nutrient availability. To achieve precise control of substrate conditions, this module is equipped with a salinity sensor network and a nutrient injection system. The salinity sensors monitor the substrate's salinity concentration in real time using conductivity and ion-selective electrode methods, and are coupled with the automated nutrient injection system to ensure that the substrate's conductivity remains within the optimal range of 3-6 dS / m. Through intelligent algorithm optimization and dynamic control, this module can provide a suitable growth environment under different tidal cycles and seasonal variations.

[0016] The core task of this module is to achieve efficient planting of mangrove seedlings and full life-cycle monitoring of their growth status. A combination of mechanized planting equipment (such as mangrove seedling mechanized planting machines) and manual replanting ensures the uniformity and precision of large-scale planting. Multispectral drones, in conjunction with vegetation growth monitoring sensors (NDVI, EVI, PRI, etc.), collect remote sensing data to achieve real-time monitoring of vegetation cover, photosynthetic efficiency, and biomass accumulation. The multispectral sensors on the drones can identify the health status and growth potential of seedlings by observing changes in reflectance in specific wavelength bands. Furthermore, the integration of the vegetation monitoring system with IoT networks provides more refined data support, enabling precise analysis and prediction of seedling growth in both spatial and temporal dimensions.

[0017] Tidal dynamics are a key ecological factor influencing mangrove growth and expansion. 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 height, tidal range, tidal current rate, and sediment movement. Through the combination of CFD (Computational Fluid Dynamics) simulation and 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 selectively remove or replenish sediment deposits, enhancing tidal exchange and water quality renewal efficiency, thereby improving the hydrodynamic conditions and nutrient availability of mangrove habitats.

[0018] This module aims to restore ecosystem diversity and enhance stability by constructing a mangrove-fish and shrimp habitat co-construction system. The habitat construction platform includes artificial reefs, floating perches, and underwater refuges, providing suitable habitats for various aquatic organisms. Simultaneously, aquatic organism monitoring devices (such as sonar detectors and water quality sensors) are used to monitor population dynamics, behavioral patterns, and habitat changes in real time. To promote biodiversity restoration and stability, this module also introduces a microbial injection system, adding specific symbiotic microorganisms and nitrogen-fixing bacteria to improve substrate structure and nutrient cycling efficiency. Experiments show that this method can significantly increase the species richness and community diversity of benthic animals, enhancing the stability and resilience of the ecosystem.

[0019] To achieve intelligent management of mangrove ecological restoration, this invention constructs a monitoring and feedback control system based on Internet of Things (IoT) and Artificial Intelligence (AI) technologies. All sensor nodes transmit data via wireless networks (such as LoRa or NB-IoT) and perform real-time processing and decision-making through an edge computing module. The system utilizes deep learning algorithms (such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to predict and optimize vegetation growth status, tidal dynamics, and biodiversity. The feedback control module dynamically adjusts matrix restoration, tidal regulation, and biodiversity building based on data analysis results at different time scales, thereby achieving refined management of the ecosystem restoration process.

[0020] In pilot areas along the coast of Hainan and Guangdong, the mangrove ecological restoration system provided by this invention has been successfully applied to multiple damaged areas. Through two years of dynamic monitoring and assessment, vegetation cover increased by 35.4%, and net primary productivity (NPP) of plants increased by 28.7%. Biodiversity indicators improved significantly, with fish and shrimp population density increasing by 42.6%. The application of an intelligent monitoring and feedback control system significantly enhanced the efficiency and sustainability of ecological restoration. Furthermore, the system's modular design provides excellent adaptability and scalability, offering a novel technological approach for the restoration and protection of coastal wetland ecosystems.

[0021] In this embodiment of the invention, a mangrove seedling mechanical planting machine is used for large-scale planting. Vegetation cover data of the mangrove area is acquired using a multispectral drone, and growth is monitored through vegetation indices (such as NDVI). The NDVI model is as follows:

[0022] NIR , RED These are the reflectance values ​​in the near-infrared and red light bands, respectively. Planting density and replanting areas are dynamically adjusted using vegetation growth monitoring sensors and drone data to ensure uniform vegetation restoration.

[0023] In this embodiment of the invention, the tidal dynamics control module simulates natural tides and is optimized in conjunction with a hydrodynamic simulation system. The hydrodynamics are calculated using a two-dimensional shallow water equation model.

[0024] in, Because of the water depth, Let t be the velocity vector and t be the time. It is the acceleration due to gravity. External forces (such as the influence of tidal gates) can also contribute to this. Adjusting gate opening and closing times and dredging vessel operations can optimize hydrodynamic conditions and ensure suitable water flow around the mangrove roots.

[0025] The intelligent ecological matrix restoration module includes the following signal processing steps: (1) Data acquisition: The salt sensor monitors the salt concentration and distribution in the matrix in real time and outputs an electrical signal. The signal is converted into a digital signal by the analog-to-digital converter and transmitted to the control system. (2) Signal processing and analysis: The control system filters the salinity sensor data, removes noise signals, obtains the salinity change curve, and calculates the dynamic control target of matrix salinity based on the obtained salinity distribution data. (3) Command generation and execution: The control system sends commands to the substrate sprayer according to the set threshold of salt concentration. The substrate sprayer controls the dosage and range of the nano soil stabilizer and regulates the application ratio of organic amendment through the nutrient injection system. (4) Feedback signal monitoring: Collect salt data again and compare it 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.

[0026] The signal processing steps of the seedling planting and dynamic monitoring module include: (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, and multispectral drones acquire 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 data collected by each sensor is transmitted to the central control system through the IoT gateway. The data is integrated from multiple sources through the data fusion algorithm to remove redundancy and noise. (3) Dynamic analysis and decision-making: use multispectral images to classify vegetation health, use machine learning algorithms to assess whether seedling growth is normal, and generate instructions to adjust planting density or replant based on environmental data fed back by sensors. (4) Output and implementation: The mangrove seedling mechanical planter automatically completes the replanting of seedlings according to the generated planting density and positioning instructions, and the drone regularly re-flies to monitor, forming a closed loop of dynamic monitoring.

[0027] The tidal dynamic control module includes the following signal processing steps: (1) Hydrodynamic data acquisition: The hydrodynamic simulation system acquires tidal flow data (such as water level height, flow velocity, and flow direction) through water level sensors and flow velocity sensors, and transmits these data to the main control system in real time through telemetry equipment; (2) Data simulation and modeling: hydrodynamic data is input into hydrodynamic simulation software, and a dynamic model of the tidal region is established through finite element analysis. The system simulates the effects of different tidal regulation strategies and outputs the best tidal regulation scheme. (3) Control command generation: Based on the simulation results, send commands such as opening and closing frequency and duration to the tidal gate. If the hydrodynamic force is insufficient, the control system starts the dredging vessel to remove silt and improve the tidal conditions. (4) Feedback and adjustment: Real-time collection of hydrodynamic data after regulation and comparison with model prediction values. If the deviation exceeds the set threshold, the system recalculates and adjusts the parameters of the tidal gate to form dynamic optimization control.

[0028] The biodiversity reconstruction module includes the following signal processing steps: (1) Ecological monitoring data collection: Aquatic organism 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 substrate in real time. (2) Signal analysis and model building: The data processing unit classifies and clusters the data from the aquatic organism monitoring device and microbial sensor, establishes a biodiversity index model, and assesses the ecological health level of the mangrove area; (3) Ecological regulation command generation: The system sends commands to the habitat construction platform based on the biodiversity index to optimize the structure of fish and shrimp habitats. 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 regulation effect, and optimize ecological regulation parameters based on feedback data to ensure continuous improvement of biodiversity.

[0029] Through the aforementioned signal processing steps, the various modules work synergistically to achieve dynamic management of the mangrove ecosystem, from matrix restoration to biodiversity reconstruction. This 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.

[0030] like Figure 2As shown in the figure, an embodiment of the present invention provides a method for restoring and reconstructing a damaged mangrove ecosystem, which specifically includes: S1: Intelligent Ecological Matrix Restoration S11: Matrix survey and analysis, using salinity sensors and soil sampling devices to obtain the salinity distribution, pH and organic matter content of the regional matrix; S12: Soil conditioner spraying. Nano soil stabilizer is evenly sprayed using a substrate spraying machine to improve the structural stability of the substrate and reduce salt volatilization rate; 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; S14: Real-time monitoring and feedback, dynamically monitor changes in soil salinity, adjust salinity control thresholds in real time, and prevent secondary soil salinization. S2: Seedling Planting and Dynamic Monitoring S21: Seedling selection: Choose mangrove plant species suitable for local saline-alkali conditions (such as Kandelia candel and Paulownia tomentosa) to ensure resistance; S22: Automatic planting, using a mangrove seedling mechanical planting machine to plant at equal intervals, and optimizing the planting density according to the terrain characteristics; S23: Intelligent monitoring, deploying vegetation growth sensors, and using 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 the risk of tidal erosion and deploys protective measures in advance.

[0031] S3: Tidal Dynamic Regulation S31: Hydrodynamic modeling, using a hydrodynamic simulation system to simulate tidal flow in mangrove areas and find the best hydrodynamic improvement scheme; S32: Artificial regulation involves setting up tidal gates at key tidal break points to control water flow and restore the natural flooding rhythm of mangroves; S33: Dredging and renovation, dredging areas with severe sedimentation to improve hydrodynamic conditions and mitigate the adverse effects of siltation on seedling survival; S34: Dynamic adjustment, real-time monitoring of tidal water level changes, adjustment of gate opening frequency and duration to maintain regional hydrodynamic balance; S4: Biodiversity Restoration S41: Habitat design, creating habitat platforms for aquatic organisms such as fish and shrimp in mangrove areas to enhance ecosystem diversity; S42: Microbial injection, using a microbial injection system to inject specific symbiotic microorganisms into the matrix to enhance the nitrogen fixation capacity of mangrove roots; S43: Enhanced ecological connectivity by establishing pathways to improve connectivity between mangrove areas and surrounding ecosystems, thereby promoting species migration and resource flow; S44: Long-term monitoring, using aquatic organism monitoring devices to assess changes in fish and shrimp populations, and dynamically adjusting habitat optimization plans.

[0032] Example 1: Restoration of mangrove ecosystems after typhoon damage In a coastal area, a typhoon caused extensive damage to mangrove forests, leading to increased surface salinity, soil structure disruption, a deterioration of the mangrove seedlings' growing environment, and a significant decline in biodiversity. Traditional restoration methods have proven ineffective due to a lack of dynamic monitoring and precise control.

[0033] (1) Intelligent ecological matrix remediation: Nano soil stabilizers and organic amendments are evenly sprayed onto the damaged area using a matrix sprayer, and the soil salinity concentration is monitored in real time by a salinity sensor. Based on the sensor feedback data, the system dynamically adjusts the nutrient injection ratio to ensure that the matrix salinity is reduced to an appropriate range.

[0034] (2) Seedling planting and dynamic monitoring: Mangrove seedlings were planted in the restored substrate at a set density using a mangrove seedling mechanical planting machine. The coverage and health status of the mangrove seedlings were monitored by drone remote sensing, and growth data was collected in real time through IoT sensors.

[0035] (3) Tidal dynamic regulation: The system monitors tidal dynamics through water level sensors and uses tidal gates to regulate hydrodynamic conditions to prevent excessive sedimentation or high salinity. In areas with insufficient hydrodynamics, dredging vessels are activated to optimize water flow channels and improve the growth environment of mangrove seedlings.

[0036] (4) Biodiversity restoration: Specific symbiotic microorganisms are introduced using a microbial injection system to promote ecological cycles within the substrate. Habitat construction platforms are installed in the damaged area to provide shelter for aquatic organisms such as fish and shrimp, gradually restoring biodiversity.

[0037] Soil salinity has decreased to levels suitable for mangroves.

[0038] The survival rate of mangrove seedlings has increased to 90%, and the vegetation coverage has increased significantly.

[0039] Fish and shrimp populations have recovered to pre-disaster levels, and the biodiversity index has increased by 40%.

[0040] Example 2: Mangrove Pollution Control After Industrial Wastewater Discharge Due to long-term wastewater discharge, mangrove forests in a certain industrial area have been polluted, hydrodynamics have been disrupted, a large number of mangrove seedlings have died, fish and shrimp habitats have been destroyed, and the ecological function has been severely degraded.

[0041] (1) Intelligent ecological matrix remediation: The matrix is ​​comprehensively tested using a salinity sensor and pollutant monitoring equipment. Nano soil stabilizers and organic amendments are applied by a matrix sprayer, while nutrient solution is dynamically injected to decompose and adsorb harmful substances in the matrix.

[0042] (2) Seedling planting and dynamic monitoring: Mechanical planting machines are used to plant mangrove seedlings with strong pollution resistance. Drones and IoT sensors work together to monitor the health of vegetation, record seedling growth data, and identify areas of abnormal growth caused by pollution.

[0043] (3) Tidal dynamic regulation: Install tidal gates to simulate natural tidal flow and remove pollutants. Use a hydrodynamic simulation system to optimize water flow patterns and reduce the deposition of industrial pollutants in mangrove areas.

[0044] (4) Biodiversity restoration: Injecting specific microbial communities to enhance pollutant degradation efficiency. Habitat construction platforms restore the habitat of fish and shrimp, gradually attracting aquatic populations back.

[0045] The pollutant content in mangrove matrix has been reduced to below environmental standards.

[0046] The survival rate of mangrove seedlings reached over 85%, and the vegetation coverage in the polluted area was restored to 75% of its original level.

[0047] The aquatic ecosystem is gradually recovering, the population of fish and shrimp is increasing, and the ecological function is significantly improved.

[0048] The above description is merely 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 those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within 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: The intelligent ecological matrix remediation module includes a matrix sprayer, a salinity sensor, and a nutrient injection system, which is used to combine nano-soil stabilizers with organic amendments and to dynamically regulate matrix salinity. The seedling planting and dynamic monitoring module includes equipment such as a mangrove seedling mechanical planting machine, vegetation growth monitoring sensors, and a multispectral drone, which is used for real-time monitoring based on drone remote sensing and IoT sensors. The tidal dynamic control module includes equipment such as tidal gates, dredging vessels, and hydrodynamic simulation systems, which are used to simulate natural tides and improve hydrodynamic conditions in conjunction with artificial structures. The biodiversity reconstruction module includes equipment such as a habitat construction platform, aquatic life monitoring devices, and a microbial injection system, for the co-construction of mangrove-fish and shrimp habitats. The intelligent ecological matrix restoration module includes the following signal processing steps: (1) Data acquisition: The salt sensor monitors the salt concentration and distribution in the matrix in real time and outputs an electrical signal. The signal is converted into a digital signal by the analog-to-digital converter and transmitted to the control system. (2) Signal processing and analysis: The control system filters the salinity sensor data, removes noise signals, obtains the salinity change curve, and calculates the dynamic control target of matrix salinity based on the obtained salinity distribution data. (3) Command generation and execution: The control system sends commands to the substrate sprayer according to the set threshold of salt concentration. The substrate sprayer controls the dosage and range of the nano soil stabilizer and regulates the application ratio of organic amendment through the nutrient injection system. (4) Feedback signal monitoring: Collect salt data again and compare it 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. The signal processing steps of the seedling planting and dynamic monitoring module include: (1) Data collection: vegetation growth monitoring sensors collect growth parameters and environmental parameters of mangrove seedlings, and multispectral drones acquire 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 data collected by each sensor is transmitted to the central control system through the IoT gateway. The data is integrated from multiple sources through the data fusion algorithm to remove redundancy and noise. (3) Dynamic analysis and decision-making: use multispectral images to classify vegetation health, use machine learning algorithms to assess whether seedling growth is normal, and generate instructions to adjust planting density or replant based on environmental data fed back by sensors. (4) Output and implementation: The mangrove seedling mechanical planter automatically completes the replanting of seedlings according to the generated planting density and positioning instructions, and the drone regularly re-flies to monitor, forming a closed loop of dynamic monitoring; The tidal dynamic control module includes the following signal processing steps: (1) Hydrodynamic data acquisition: The hydrodynamic simulation system acquires tidal flow data through water level sensors and flow velocity sensors, and transmits these data to the main control system in real time through telemetry equipment; (2) Data simulation and modeling: hydrodynamic data is input into hydrodynamic simulation software, and a dynamic model of the tidal region is established through finite element analysis. The system simulates the effects of different tidal regulation strategies and outputs the best tidal regulation scheme. (3) Control command generation: Based on the simulation results, send the opening and closing frequency and duration commands to the tidal gate. If the hydrodynamic force is insufficient, the control system starts the dredging vessel to remove silt and improve the tidal conditions. (4) Feedback and adjustment: Real-time collection of hydrodynamic data after regulation and comparison with model prediction values. If the deviation exceeds the set threshold, the system recalculates and adjusts the parameters of the tidal gate to form dynamic optimization control. The biodiversity reconstruction module includes the following signal processing steps: (1) Ecological monitoring data collection: Aquatic organism monitoring devices capture the population size, distribution and activity status of fish and shrimp aquatic organisms, and microbial sensors monitor the activity and quantity of microorganisms in the substrate in real time. (2) Signal analysis and model building: The data processing unit classifies and clusters the data from the aquatic organism monitoring device and microbial sensor, establishes a biodiversity index model, and assesses the ecological health level of the mangrove area; (3) Ecological regulation command generation: The system sends commands to the habitat construction platform based on the biodiversity index to optimize the structure of fish and shrimp habitats. 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: collect ecological monitoring data regularly, evaluate the regulation effect, and optimize ecological regulation parameters based on feedback data.

2. A method for restoring and reconstructing damaged mangrove ecosystems as described in claim 1, characterized in that, The method specifically includes: S1: Intelligent Ecological Matrix Restoration S11: Matrix survey and analysis, using salinity sensors and soil sampling devices to obtain the salinity distribution, pH and organic matter content of the regional matrix; S12: Soil conditioner spraying, nano soil stabilizer is evenly sprayed through a substrate spraying machine; S13: Nutritional supplementation, injecting organic modifiers into the matrix, and controlling the application ratio of organic modifiers through a nutrient injection system to ensure that the electrical conductivity of the matrix is ​​maintained within the optimal range of 3-6 dS / m; the organic modifiers include humic acid, seaweed extract, and humic acid. S14: Real-time monitoring and feedback, dynamically monitoring changes in soil salinity, and adjusting salinity control thresholds in real time; S2: Seedling Planting and Dynamic Monitoring S21: Seedling selection: Select mangrove plant species suitable for local saline-alkali conditions; S22: Automatic planting, using a mangrove seedling mechanical planting machine to plant at equal intervals, and optimizing the planting density according to the terrain characteristics; S23: Intelligent monitoring, deploying vegetation growth sensors, using drones to regularly collect data on vegetation coverage and seedling height, and dynamically tracking growth status; S24: Disaster early warning. Based on the growth parameters and environmental parameters collected by vegetation growth monitoring sensors, the vegetation coverage and reflectance spectrum information obtained by multispectral drones, and the tidal flow data obtained by water level sensors and flow velocity sensors, analyze the risk of tidal erosion and deploy protective measures in advance. S3: Tidal Dynamic Regulation S31: Hydrodynamic modeling, using a hydrodynamic simulation system to simulate tidal flow in mangrove areas and find the best hydrodynamic improvement scheme; S32: Artificial regulation involves setting up tidal gates at key tidal break points to control water flow and restore the natural flooding rhythm of mangroves; S33: Dredging and modification, dredging areas with severe sedimentation; S34: Dynamic adjustment, real-time monitoring of tidal water level changes, adjustment of gate opening frequency and duration to maintain regional hydrodynamic balance; S4: Biodiversity Restoration S41: Habitat design, creating habitat platforms for fish and shrimp in mangrove areas; S42: Microbial injection, using a microbial injection system to inject symbiotic microorganisms and nitrogen-fixing bacteria into the substrate to improve substrate structure and nutrient cycling efficiency; S43: Enhanced ecological connectivity by establishing pathways to improve connectivity between mangrove areas and surrounding ecosystems; S44: Long-term monitoring, using aquatic organism monitoring devices to assess changes in fish and shrimp populations, and dynamically adjusting habitat optimization plans.

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