An ecological restoration planning monitoring method and system based on multi-source data technology

Abstract

The application relates to the technical field of ecological restoration, and discloses an ecological restoration planning monitoring method and system based on multi-source data technology, aiming to solve the problems of insufficient perception of dynamic changes of an ecological system and lag of restoration planning management in existing ecological restoration monitoring due to static dispersion of traditional means and missing of multi-source heterogeneous data integration. The application comprises the following modules: multi-source heterogeneous data acquisition preprocessing, multi-modal data fusion feature extraction, ecological system dynamic state modeling evaluation, ecological restoration planning dynamic optimization, and real-time monitoring and effect evaluation, and realizes closed-loop management through feedback. Through the above technical scheme, the application can overcome the limitations of traditional monitoring, realize deep data fusion, improve dynamic optimization of planning and precision of decision-making, and significantly improve the scientificity and effectiveness of ecological restoration.

Description

Technical Field

[0001] This invention belongs to the field of ecological restoration technology, and more specifically, relates to an ecological restoration planning and monitoring method and system based on multi-source data technology. Background Technology

[0002] For a considerable period, the evaluation and management of ecological restoration effectiveness relied primarily on traditional monitoring methods. Periodic field surveys were the most fundamental and direct approach, designed to acquire detailed data on key ecosystem components (such as vegetation cover, soil physicochemical properties, water quality indicators, and animal populations) through field investigations, sample collection, biodiversity counting, and manual measurements. This method, with its high precision and deep insight into specific details, effectively addressed the problem of assessing the ecological status of local areas at specific points in time, providing a direct basis for developing preliminary restoration plans, given the historical conditions at the time. Simultaneously, single-image remote sensing analysis was gradually introduced into the field of ecological monitoring. Its core technology involves using satellites or airborne platforms equipped with sensors to acquire surface electromagnetic wave information and extracting macroscopic information such as the National Distance Index (NDVI), water distribution, and land use types through image processing and spectral analysis. This method, with its wide coverage and non-contact nature, overcame the limitations of time-consuming and labor-intensive manual surveys to some extent, enabling preliminary assessments of large-scale regional ecological landscape patterns and providing data support for macro-planning. These traditional methods have played a positive role in their respective application scenarios, accumulating valuable experience for early ecological restoration practices.

[0003] In summary, the core technological challenges facing the field of ecological restoration monitoring and management have evolved from the inadequacy of acquiring single data sources to the effective integration and intelligent utilization of multi-source heterogeneous data. This aims to overcome the inherent limitations of traditional static monitoring and construct an integrated method capable of adapting to the dynamic complexity of ecosystems, achieving precise perception, real-time feedback, and intelligent regulation of the restoration process. Therefore, developing an intelligent monitoring and management method based on deep fusion of multi-source data, capable of dynamically optimizing and adjusting ecological restoration plans, has become a key challenge and a pressing technical problem for those skilled in the art. Summary of the Invention

[0004] To achieve the above objectives, this application provides the following technical solution:

[0005] According to a first aspect of the present invention, the present invention claims protection for an ecological restoration planning and monitoring method based on multi-source data technology, characterized by comprising the following steps:

[0006] S1, Construct a multi-source heterogeneous data acquisition and preprocessing module to acquire and standardize various types of ecological monitoring data, including remote sensing data, UAV aerial photography data, ground sensor network data, and auxiliary geographic information and socio-economic data;

[0007] S2, Construct a multimodal data fusion and feature extraction module to perform deep fusion and high-dimensional feature extraction on the heterogeneous data obtained in step S1 to generate a comprehensive ecological feature representation;

[0008] S3, Construct an ecosystem dynamic state modeling and assessment module, based on the comprehensive ecological characteristics extracted in step S2, establish an ecosystem dynamic model and assess its health status and service functions;

[0009] S4. Construct an ecological restoration planning and dynamic optimization module. Based on the evaluation results of step S3, generate an initial ecological restoration plan and make adaptive adjustments based on real-time monitoring feedback.

[0010] S5. Construct a real-time monitoring and effect evaluation module to continuously acquire real-time data during the ecological restoration process, evaluate the restoration progress and effect, and feed the evaluation results back to the ecological restoration planning and dynamic optimization module to form a closed-loop management.

[0011] Furthermore, in step S1, the multi-source heterogeneous data acquisition and preprocessing module specifically includes:

[0012] The remote sensing data acquisition unit is used to acquire remote sensing image data with different spatial resolutions, spectral characteristics and temporal resolutions, including multispectral data provided by high-resolution optical remote sensing satellites, radar image data provided by synthetic aperture radar (SAR) satellites and lidar (LiDAR) data.

[0013] The UAV aerial photography data acquisition unit is used to carry multiple types of sensors to acquire ultra-high resolution near-surface data. The sensor types include high-resolution visible light cameras, multispectral or hyperspectral cameras, thermal infrared cameras, and UAV-borne lidar systems.

[0014] The ground sensor network acquisition unit is used to deploy distributed, real-time monitoring sensor nodes in the restoration area. The sensor nodes include soil sensors, water quality sensors, biological monitoring sensors, and climate and meteorological stations.

[0015] The auxiliary geographic information and socio-economic data acquisition unit is used to integrate high-precision digital elevation model (DEM), land use cover type map, administrative division boundaries, water system distribution, road network, population density distribution map, regional economic activity data, and relevant policy and regulatory documents;

[0016] The data preprocessing unit is used to perform data format standardization and coordination, geometric correction and registration, radiometric correction and atmospheric correction, noise removal and cloud / shadow removal, missing data imputation, and data quality assessment on the multi-source heterogeneous data collected above.

[0017] Furthermore, the preprocessing operations performed by the data preprocessing unit include:

[0018] Data format standardization and harmonization unify data from different sources and in different formats into a standardized geospatial data format, and unify spatial reference systems and projection methods;

[0019] Geometric correction and registration: Perform geometric fine correction on remote sensing imagery and UAV imagery to eliminate geometric distortions caused by terrain, sensor attitude and Earth curvature, and perform spatial registration with high-precision geographic base maps;

[0020] Radiometric correction and atmospheric correction: Radiometric correction is performed on optical remote sensing images to convert the digital quantization values ​​recorded by the sensor into surface reflectance, and atmospheric correction is performed to eliminate the effects of atmospheric scattering and absorption.

[0021] Noise removal and cloud / shadow removal are achieved by using image filtering algorithms to remove image noise.

[0022] For optical images, cloud removal and shadow removal algorithms based on time series analysis or spectral features are used to restore the surface information covered by clouds.

[0023] For missing data imputation, data reconstruction is performed using time series prediction models or spatial interpolation algorithms to address the lack of monitoring data due to sensor failure or external interference.

[0024] Furthermore, in step S2, the multimodal data fusion and feature extraction module specifically includes:

[0025] A data heterogeneity processing unit is used to overcome the differences in spatial resolution, temporal frequency, spectral characteristics and data structure of multi-source data. The processing includes spatial resolution unification, timestamp alignment and feature dimension unification.

[0026] The multi-level feature extraction unit is used to extract multi-dimensional and multi-scale ecological features from preprocessed data, including spectral features, texture features, geometric and morphological features, time series features, and statistical features.

[0027] A deep multimodal data fusion unit, which adopts a deep learning-based architecture, realizes deep fusion and information complementarity among multi-source heterogeneous data, and generates a high-dimensional, semantically rich comprehensive ecological feature representation;

[0028] The output of the fusion and feature extraction module is a unified, high-dimensional set of fused feature vectors.

[0029] Furthermore, in step S3, the ecosystem dynamic state modeling and assessment module specifically includes:

[0030] An ecological index calculation unit is used to calculate a series of quantitative ecological indicators based on the fusion features output in step S2. The indicators include vegetation ecological indicators, hydrological ecological indicators, soil ecological indicators, biodiversity indicators, and the ecosystem health index (EHI).

[0031] The ecosystem service function quantification unit is used to quantitatively assess the ecosystem service functions within the restoration area. These service functions include carbon sequestration and oxygen release, water conservation, soil retention, biodiversity maintenance, and air purification.

[0032] An ecosystem dynamic evolution model unit is used to construct and run ecosystem dynamic evolution prediction models to predict future trends of ecological indicators and identify potential risks. The models include time series prediction models, state-space models, and scenario simulation models.

[0033] It outputs real-time updated ecosystem health status assessment reports, quantitative results of various ecosystem service functions, prediction maps of future dynamic evolution of the ecosystem, and early warning information on potential risks.

[0034] Furthermore, in step S4, the ecological restoration planning and dynamic optimization module specifically includes:

[0035] The ecological restoration target setting unit is used to set specific, quantifiable, and temporally and spatially differentiated ecological restoration targets based on national and local ecological and environmental protection strategies, regional ecological baseline conditions, degree of degradation, and socio-economic development needs.

[0036] The spatial zoning and priority ranking unit is used to utilize the spatial analysis technology of Geographic Information System (GIS) and combine it with the ecosystem health assessment results output in step S3 to conduct refined ecological functional zoning of the restoration area and determine the restoration priority and appropriate restoration measures for each zoning.

[0037] A smart repair measure recommendation unit is used to intelligently recommend a combination of targeted repair measures based on the spatial partitioning results and repair priorities; and

[0038] The dynamic adjustment and optimization strategy unit is used to adaptively adjust and optimize the implemented repair plan based on the real-time monitoring feedback of step S5.

[0039] The module outputs an initial ecological restoration plan and a dynamic adjustment suggestion report generated based on real-time monitoring feedback.

[0040] Furthermore, the intelligent recommendation unit for repair measures specifically includes:

[0041] A comprehensive knowledge base for ecological restoration technology is established by matching the knowledge base with the rule base. The knowledge base includes detailed descriptions of various restoration measures, applicable environmental conditions, expected ecological responses, implementation costs, cycles and risks. Based on the ecological characteristics, degradation types and restoration goals of the current zone, appropriate restoration technologies are matched through rule reasoning or expert systems.

[0042] The multi-objective optimization algorithm employs the multi-objective genetic algorithm MOGA, which uses multiple objective functions to maximize ecological benefits, minimize total restoration costs, and minimize implementation risks. It searches for the optimal combination of restoration measures on the Pareto front, generating a series of optimal solutions that balance different objectives for decision-makers to choose from.

[0043] The reinforcement learning decision-making framework defines the state of the ecosystem in different partitions and time steps as the state space of the RL environment, the feasible remediation measures as the action space of the RL agent, and the improvement of the ecological health index, the increase of ecosystem service functions, and resource consumption as the reward function. By training a deep Q-network (DQN), a policy gradient algorithm, or an Actor-Critic algorithm, the RL agent can learn an optimal policy and dynamically select and execute the optimal combination of remediation measures based on the real-time state of the ecosystem.

[0044] Furthermore, in step S5, the real-time monitoring and effect evaluation module specifically includes:

[0045] The continuous data stream management unit is used to establish a real-time data processing pipeline based on big data stream processing technology to continuously acquire, clean, integrate and standardize multi-source heterogeneous monitoring data from ground sensor networks, UAV aerial photography and remote sensing platforms.

[0046] The key ecological parameter extraction and trend analysis unit is used to extract and analyze changes in key ecological parameters from real-time data streams. These changes include real-time parameter extraction, time series trend analysis, and anomaly detection and mutation identification.

[0047] The restoration effect quantitative evaluation unit is used to conduct multi-dimensional and quantitative evaluation of the effects in the ecological restoration process. The evaluation includes baseline comparison evaluation, target achievement evaluation, and comprehensive ecological benefit accounting.

[0048] The deviation warning and reporting unit is used to generate deviation warnings and provide visual reports based on the evaluation results. The warnings and reports include a multi-level warning mechanism, warning information push, and visual report generation.

[0049] The module outputs real-time ecological restoration progress reports, key parameter trend analysis charts, quantitative assessment reports of restoration effects, and multi-level deviation early warning notifications.

[0050] Furthermore, the quantitative evaluation unit for the repair effect specifically includes:

[0051] Baseline comparison assessment involves comparing and analyzing various ecological indicators obtained from real-time monitoring with baseline data before the implementation of restoration projects from multiple dimensions to quantify restoration progress and improvement. The comparison analysis includes the percentage increase in vegetation coverage, the increase in soil organic matter content, the improvement in water quality indicators, changes in biodiversity index, and the improvement in the ecosystem health index (EHI).

[0052] The goal achievement assessment compares real-time monitoring data with the phased and final goals set in the planning scheme, calculates the goal achievement rate of each indicator, and identifies gaps and unmet areas in the repair process.

[0053] The comprehensive ecological benefit accounting adopts an ecosystem service value assessment model to qualitatively and quantitatively calculate the ecological benefits brought about by restoration, including water conservation increment, biodiversity protection effectiveness, soil retention, carbon sequestration increment, air purification benefits, and landscape aesthetic value, and provides a corresponding economic value assessment report.

[0054] According to a second aspect of the present invention, the present invention claims protection for an ecological restoration planning and monitoring system based on multi-source data technology, comprising:

[0055] A multi-source heterogeneous data acquisition and preprocessing module, which is used to acquire and standardize various types of ecological monitoring data;

[0056] A multimodal data fusion and feature extraction module, the module being used to perform deep fusion and high-dimensional feature extraction on the heterogeneous data;

[0057] An ecosystem dynamic state modeling and assessment module is used to establish an ecosystem dynamic model and assess its health status and service functions based on the fusion features;

[0058] An ecological restoration planning and dynamic optimization module is used to generate an initial ecological restoration plan based on the assessment results and to make adaptive adjustments based on real-time monitoring feedback.

[0059] The real-time monitoring and effect evaluation module is used to continuously acquire real-time data during the ecological restoration process, evaluate the restoration progress and effect, and feed the evaluation results back to the ecological restoration planning and dynamic optimization module.

[0060] The system also includes a data bus for enabling seamless data transmission and collaborative operation between modules;

[0061] The system also includes a unified geospatial database for storing all processed geospatial data, metadata, and planning schemes; and

[0062] The system also includes a visualization and decision support platform, which provides an intuitive user interface for displaying real-time monitoring data, evaluation results, planning schemes and adjustment suggestions, supporting managers to make human-machine collaborative decisions.

[0063] The aforementioned ecological restoration planning and monitoring system based on multi-source data technology is used to execute the aforementioned ecological restoration planning and monitoring method based on multi-source data technology.

[0064] Through the above technical solution, the present invention achieves the following technical effects:

[0065] Overcoming the limitations of static and discrete traditional monitoring: This invention integrates multi-source data from remote sensing satellites, drones, ground sensor networks, and weather stations, and is supplemented by efficient data stream management to achieve continuous, multi-scale, and multi-dimensional real-time monitoring of the dynamic changes of the ecosystem. It breaks through the bottleneck of traditional "snapshot" observation and can accurately capture the subtle changes in the ecosystem in the short period of time and the key nodes in the long-term succession process.

[0066] This invention solves the challenge of multi-source heterogeneous data fusion: It constructs a deep learning-based multimodal data fusion and feature extraction module. Through multi-branch encoder networks, cross-modal attention mechanisms, and spatiotemporal context fusion technology, it effectively overcomes the "data silo" problem caused by heterogeneous data from different sources, formats, and scales. It achieves deep fusion and information complementarity among multi-source data, generating high-dimensional and semantically rich comprehensive ecological features, providing high-quality input for subsequent accurate modeling.

[0067] This invention achieves dynamic optimization and adaptive management of ecological restoration planning: By constructing an ecosystem dynamic state modeling and assessment module, it accurately quantifies ecological health and service functions and predicts their dynamic evolution trends. Based on this, the ecological restoration planning and dynamic optimization module combines multi-objective optimization algorithms and a reinforcement learning decision framework. It can scientifically and dynamically adjust restoration plans based on real-time monitoring feedback, ensuring a high degree of matching between restoration measures and actual ecological needs. This significantly improves the scientific rigor, effectiveness, and resource efficiency of restoration outcomes, transforming the restoration management model from a traditional static linear model to a closed-loop, adaptive, and intelligent management model.

[0068] This invention enhances the accuracy and foresight of ecological restoration decision-making: By integrating multi-source data, dynamic modeling, scenario simulation, and predictive analysis, it provides a comprehensive and in-depth understanding of the ecosystem's state and the ability to predict future trends. The deviation early warning mechanism of the real-time monitoring and effect evaluation module can promptly identify problems in the restoration process, triggering planning adjustments. This makes restoration decisions more accurate, adaptable, and forward-looking, effectively reducing restoration risks and ensuring the achievement of restoration goals. Attached Figure Description

[0069] Figure 1 The flowchart is for an ecological restoration planning and monitoring method based on multi-source data technology, which is claimed in this invention.

[0070] Figure 2 This is a second workflow diagram of an ecological restoration planning and monitoring method based on multi-source data technology, for which protection is claimed in this invention.

[0071] Figure 3 The third workflow diagram is for an ecological restoration planning and monitoring method based on multi-source data technology, which is claimed in this invention.

[0072] Figure 4 This is a structural block diagram of an ecological restoration planning and monitoring system based on multi-source data technology, for which protection is sought in this invention. Detailed Implementation

[0073] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0074] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0075] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0076] As research into the complexity of ecosystems deepens, the rate of environmental change accelerates, and the investment-efficiency ratio of ecological restoration projects becomes increasingly important, the inherent limitations of the aforementioned traditional methods are becoming more apparent, gradually evolving into a deep-seated technical contradiction. The reason for this lies in the fact that an ecosystem is a highly dynamic, complex, and multi-scale open system. Its internal elements (such as hydrology, soil, vegetation, and animal communities) exhibit intricate nonlinear interactions and are constantly affected by external environmental disturbances (such as climate and human activities). Traditional monitoring methods, whether manual surveys or single remote sensing analysis, are essentially static, discrete, snapshot-like observations. They are either limited by sampling frequency and spatial coverage, resulting in long data update cycles and poor timeliness, failing to capture subtle changes in the ecosystem within short periods and key nodes in long-term succession; or they suffer from limited data dimensions, making it difficult to comprehensively reveal the coupling mechanisms and complex feedback paths of multiple elements within the ecosystem. For example, while a single remote sensing image can provide vegetation cover information, it is difficult to directly reflect deeper ecological indicators such as soil moisture, groundwater dynamics, and biodiversity, let alone characterize the linkage effects between these indicators. This "snapshot"-based assessment paradigm suffers from inherent limitations in its interpretation and decision-making when faced with the dynamic evolution and high uncertainty of ecosystems.

[0077] Furthermore, this inherent, fundamental flaw leads to more complex "secondary contradictions" in the practical application of existing technologies. With the rapid development of advanced data acquisition technologies such as satellite remote sensing, drone aerial photography, ground sensor networks, and meteorological observation, we now possess massive amounts of multi-dimensional, multi-temporal resolution ecological monitoring data sources. Theoretically, this data can provide unprecedented support for the dynamic monitoring and refined management of ecosystems. However, existing technological paradigms, especially the underlying data processing and information integration logic, have failed to achieve effective collaboration and deep integration of these multi-source data. Heterogeneous data from different sources, in different formats, and at different scales, like scattered pearls, exist independently, lacking efficient connection and transformation mechanisms, forming insurmountable "data silos." The core issue is that existing technologies have not yet constructed a comprehensive analytical framework capable of overcoming data heterogeneity barriers and deeply mining the inherent correlations between multi-source data. This lack of a framework makes it difficult to transform the abundant data into decision-making information with clear guiding significance that comprehensively reflects the health status and evolution trends of ecosystems. As a result, even with multi-source data, restoration planning and management are still forced to revert to a model relying on limited and partial information, or can only perform simple overlay analysis rather than deep fusion. This disconnect is not only reflected in the "island" effect at the data level, but also, more profoundly, in the static linear model of "planning-implementation-acceptance" in restoration management philosophy. Under this model, once a restoration plan is formulated, there is a lack of a flexible mechanism for scientific and dynamic adjustment based on real-time monitoring data during the long implementation cycle. When problems arise at the restoration site, such as slower-than-expected vegetation restoration, significant changes in soil conditions, or the impact of sudden extreme weather events, the existing management system, due to its sluggish and rigid feedback loop, struggles to respond promptly and accurately. This leads to a disconnect between restoration measures and actual ecological needs, ultimately affecting restoration effectiveness and resulting in inefficient resource investment.

[0078] According to a first embodiment of the present invention, the present invention claims protection for an ecological restoration planning and monitoring method based on multi-source data technology, referring to... Figure 1 This includes the following steps:

[0079] S1, Construct a multi-source heterogeneous data acquisition and preprocessing module to acquire and standardize various types of ecological monitoring data, including remote sensing data, UAV aerial photography data, ground sensor network data, and auxiliary geographic information and socio-economic data;

[0080] S2, Construct a multimodal data fusion and feature extraction module to perform deep fusion and high-dimensional feature extraction on the heterogeneous data obtained in step S1 to generate a comprehensive ecological feature representation;

[0081] S3, Construct an ecosystem dynamic state modeling and assessment module, based on the comprehensive ecological characteristics extracted in step S2, establish an ecosystem dynamic model and assess its health status and service functions;

[0082] S4. Construct an ecological restoration planning and dynamic optimization module. Based on the evaluation results of step S3, generate an initial ecological restoration plan and make adaptive adjustments based on real-time monitoring feedback.

[0083] S5. Construct a real-time monitoring and effect evaluation module to continuously acquire real-time data during the ecological restoration process, evaluate the restoration progress and effect, and feed the evaluation results back to the ecological restoration planning and dynamic optimization module to form a closed-loop management.

[0084] Furthermore, in step S1, the multi-source heterogeneous data acquisition and preprocessing module specifically includes:

[0085] The remote sensing data acquisition unit is used to acquire remote sensing image data with different spatial resolutions, spectral characteristics and temporal resolutions, including multispectral data provided by high-resolution optical remote sensing satellites, radar image data provided by synthetic aperture radar (SAR) satellites and lidar (LiDAR) data.

[0086] The UAV aerial photography data acquisition unit is used to carry multiple types of sensors to acquire ultra-high resolution near-surface data. The sensor types include high-resolution visible light cameras, multispectral or hyperspectral cameras, thermal infrared cameras, and UAV-borne lidar systems.

[0087] The ground sensor network acquisition unit is used to deploy distributed, real-time monitoring sensor nodes in the restoration area. The sensor nodes include soil sensors, water quality sensors, biological monitoring sensors, and climate and meteorological stations.

[0088] The auxiliary geographic information and socio-economic data acquisition unit is used to integrate high-precision digital elevation model (DEM), land use cover type map, administrative division boundaries, water system distribution, road network, population density distribution map, regional economic activity data, and relevant policy and regulatory documents;

[0089] The data preprocessing unit is used to perform data format standardization and coordination, geometric correction and registration, radiometric correction and atmospheric correction, noise removal and cloud / shadow removal, missing data imputation, and data quality assessment on the multi-source heterogeneous data collected above.

[0090] In this embodiment, the remote sensing data acquisition unit deploys various optical and radar remote sensing satellite resources. For example, high-resolution optical remote sensing satellites, such as the EU's Sentinel-2, can provide multispectral data across 13 bands, including blue, green, red, and near-infrared, with a spatial resolution of up to 10 meters and a high revisit cycle of 5 days. This allows the unit to periodically acquire key information such as surface vegetation cover, leaf area index (LAI), normalized difference vegetation index (NDVI), surface water distribution, and land use types over large areas. Furthermore, China's Gaofen series satellites can supplement this, providing sub-meter or even higher spatial resolution optical imagery for more refined land cover identification and change detection. Simultaneously, the unit also integrates synthetic aperture radar (SAR) satellite data, such as the EU's Sentinel-1 and Germany's TerraSAR-X. Sentinel-1 provides data in C-band, dual-polarization mode, with a spatial resolution of up to 5 meters. It has the ability to penetrate clouds and part of the vegetation canopy, making it particularly suitable for monitoring information that is difficult to obtain through optical remote sensing, such as soil moisture, vegetation biomass, and surface roughness. It exhibits unique advantages, especially in cloudy and rainy areas. To acquire high-precision three-dimensional surface structure information, this unit also utilizes LiDAR data, including airborne and spaceborne LiDAR systems. Airborne LiDAR systems conduct flight operations in key ecological restoration areas, typically achieving point cloud data acquisition at densities of 5 to 10 points per square meter or even higher. Processing this point cloud data allows for the precise generation of detailed three-dimensional parameters such as digital elevation models (DEM), digital surface models (DSM), vegetation height, canopy structure, understory topography, and forest biomass, providing direct evidence for terrain restoration and vegetation structure optimization.

[0091] The drone aerial data acquisition unit serves as a crucial supplement to remote sensing data, providing ultra-high-resolution near-surface data. This unit is equipped with various types of sensors to adapt to different monitoring needs. For example, high-resolution visible light cameras are typically mounted on drone platforms such as the DJI Phantom 4 RTK or DJI Matrice 300 RTK. With the aid of preset flight paths and ground control points, they can acquire orthophotos with centimeter-level spatial resolution. These images are essential for accurately identifying surface vegetation types, seedling survival rates, soil erosion patches, small-scale water pollution areas, and construction traces, and can be used for refined detection of ground feature changes. In addition, this unit is also equipped with multispectral or hyperspectral cameras, such as the MicaSense RedEdge-MX or Headwall Nano-Hyperspec. These cameras typically have multiple narrow bands, including blue, green, red, near-infrared, and red-edge bands, enabling them to more sensitively capture changes in vegetation physiological and biochemical parameters, thereby assessing vegetation health, providing early warnings of pests and diseases, assessing water eutrophication levels, and detecting the distribution of specific elements. To monitor surface temperature distribution and analyze the hydrothermal conditions of the ecosystem and the urban heat island effect, this unit is also equipped with thermal infrared cameras such as the FLIR Vue Pro, capable of acquiring thermal images with a resolution of 0.1°C. For detailed three-dimensional information of local areas, the UAV-borne lidar system provides high-density point cloud data, which can be used to accurately measure the amount of soil erosion caused by vegetation height, topographic relief, and micro-topographic changes within the restoration area.

[0092] The ground-based sensor network acquisition unit deploys distributed, real-time monitoring sensor nodes within the remediation area, forming a dense observation network. Soil sensors, such as those using the time-domain reflectometry (TDR) principle, monitor soil moisture, soil temperature, soil pH, soil conductivity, and the content of key nutrients such as nitrogen, phosphorus, and potassium in real time, once per hour, providing data support for precision fertilization and irrigation management. Water quality sensors, such as the YSI Exo series multi-parameter water quality monitor, periodically or continuously measure key water quality parameters such as pH, dissolved oxygen (DO), conductivity, turbidity, ammonia nitrogen, total phosphorus, and chlorophyll a in water bodies such as rivers, lakes, or wetlands, with accuracy and sampling frequency meeting water environment quality assessment standards. Biomonitoring sensors acquire biodiversity information non-invasively. Infrared camera traps are strategically deployed in wildlife activity areas to record the species, numbers, and activity patterns of wild animals; their trigger time, trigger interval, and recording duration are configurable. Acoustic sensors monitor the call spectra of birds and amphibians, assessing the health of the biological community through voiceprint recognition and bioacoustic index calculation. Environmental DNA sampling units periodically collect water or soil samples, analyzing the composition and diversity of the biological community using high-throughput sequencing technology, enabling effective monitoring of elusive species. Furthermore, climate and meteorological stations are deployed in key areas, monitoring local meteorological parameters such as temperature, relative humidity, precipitation, wind speed and direction, and solar radiation in real time every 10 minutes, providing environmental background data for the ecosystem's water and heat balance and biological activity. The ground sensor network utilizes wireless communication technologies such as LoRaWAN, NB-IoT, or 4G / 5G cellular networks to transmit monitoring data in encrypted form to the data aggregation center in real time, ensuring data security and timeliness.

[0093] The auxiliary geographic information and socioeconomic data acquisition unit is responsible for integrating existing macro-geospatial data and regional socioeconomic data, which provide crucial background information and constraints for ecological restoration planning. This includes, but is not limited to, high-precision digital elevation models, the latest land use / cover type maps, administrative boundaries, detailed water system distribution maps, road networks, population density distribution maps, regional economic activity data, and relevant policy and regulatory documents. The integration of this data helps to understand the natural geographical characteristics, human impacts, and policy orientations of the restoration area at a macro level.

[0094] The data preprocessing unit is a crucial step in transforming all the aforementioned data into usable information. Its function is to systematically standardize, correct, denoise, and unify the format of the collected multi-source heterogeneous data. Data format standardization and harmonization operations unify data from different sources and in different formats into a standardized geospatial data format. For example, GeoTIFF is used for image data, and NetCDF or HDF5 is used for multidimensional time-series data. Simultaneously, all data is unified to the WGS84 coordinate system and uses UTM projection to ensure a unified spatial reference benchmark across different datasets, laying the foundation for subsequent fusion and analysis. The geometric correction and registration process performs fine correction on remote sensing and UAV imagery, using orthorectification based on rigorous geometric models or polynomial models based on ground control points to eliminate geometric distortions caused by terrain undulations, sensor attitude, and Earth curvature. Simultaneously, spatial registration is performed using feature point matching algorithms and high-precision geographic base maps to ensure pixel-level accurate alignment between different data sources. Radiometric and atmospheric corrections are applied to optical remote sensing imagery. Digitally quantized values ​​are converted into apparent reflectance using sensor radiometric calibration parameters. Atmospheric correction models such as FLAASH or ATCOR, combined with atmospheric parameters, eliminate the effects of atmospheric scattering and absorption, converting apparent reflectance into true surface reflectance. Noise removal and cloud / shadow removal are crucial steps in improving image quality. Image filtering algorithms such as median filtering, Gaussian filtering, or wavelet transform are used to remove random noise from the imagery. For optical imagery, cloud / shadow removal algorithms such as Fmask, STARR-S, or time-series analysis-based algorithms are used to recover surface information obscured by clouds. Missing data interpolation mechanisms address missing monitoring data caused by sensor malfunctions or external interference. Time-series prediction models are used for temporal interpolation, or spatial interpolation algorithms are used for spatial reconstruction to ensure the integrity and continuity of the dataset. Finally, the data quality assessment stage performs a comprehensive quality check on the preprocessed data, including assessments of spatial consistency, temporal series integrity, and numerical reasonableness, ensuring high data availability.

[0095] Furthermore, referring to Figure 2 The preprocessing operations performed by the data preprocessing unit include:

[0096] Data format standardization and harmonization unify data from different sources and in different formats into a standardized geospatial data format, and unify spatial reference systems and projection methods;

[0097] Geometric correction and registration: Perform geometric fine correction on remote sensing imagery and UAV imagery to eliminate geometric distortions caused by terrain, sensor attitude and Earth curvature, and perform spatial registration with high-precision geographic base maps;

[0098] Radiometric correction and atmospheric correction: Radiometric correction is performed on optical remote sensing images to convert the digital quantization values ​​recorded by the sensor into surface reflectance, and atmospheric correction is performed to eliminate the effects of atmospheric scattering and absorption.

[0099] Noise removal and cloud / shadow removal are achieved by using image filtering algorithms to remove image noise.

[0100] For optical images, cloud removal and shadow removal algorithms based on time series analysis or spectral features are used to restore the surface information covered by clouds.

[0101] For missing data imputation, data reconstruction is performed using time series prediction models or spatial interpolation algorithms to address the lack of monitoring data due to sensor failure or external interference.

[0102] Furthermore, in step S2, the multimodal data fusion and feature extraction module specifically includes:

[0103] A data heterogeneity processing unit is used to overcome the differences in spatial resolution, temporal frequency, spectral characteristics and data structure of multi-source data. The processing includes spatial resolution unification, timestamp alignment and feature dimension unification.

[0104] The multi-level feature extraction unit is used to extract multi-dimensional and multi-scale ecological features from preprocessed data, including spectral features, texture features, geometric and morphological features, time series features, and statistical features.

[0105] A deep multimodal data fusion unit, which adopts a deep learning-based architecture, realizes deep fusion and information complementarity among multi-source heterogeneous data, and generates a high-dimensional, semantically rich comprehensive ecological feature representation;

[0106] The output of the fusion and feature extraction module is a unified, high-dimensional set of fused feature vectors.

[0107] In this embodiment, the data heterogeneity processing unit first performs a unification process on the preprocessed data. Spatial resolution unification is achieved through resampling techniques. For example, for optical images, bilinear interpolation or cubic convolution interpolation is used to unify image data of different resolutions to the target resolution, ensuring the consistency of spatial information. Timestamp alignment is achieved by using time interpolation or time window aggregation methods to synchronize ground sensor data and remote sensing image data with different acquisition frequencies on the time axis, forming consistent time-series data. Feature dimension unification is achieved through standardization or normalization. For example, NDVI values ​​are scaled to the [0,1] range, and soil moisture values ​​are Z-score standardized, so that the original features extracted from different modalities can be compared on the same numerical scale, avoiding certain features from dominating the fusion process due to their excessively large numerical range.

[0108] The multi-level feature extraction unit extracts multi-dimensional and multi-scale ecologically relevant features from the unified data. Spectral features reflect land cover composition information by calculating various vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) = (NIR - Red) / (NIR + Red), the Enhanced Vegetation Index (EVI), the Soil Modified Vegetation Index (SAVI), the Water Index (NDWI) = (Green - NIR) / (Green + NIR), and the Soil Index (BI). Texture features are calculated using the gray-level co-occurrence matrix method to extract statistics such as uniformity, contrast, correlation, and entropy of texture in the image to reflect the spatial structure and roughness information of land cover; wavelet transform can also be used to capture texture information at different scales. Geometric and morphological features are mainly extracted from LiDAR point cloud data, including vegetation height, canopy closure, vegetation density, topographic slope, aspect, and topographic roughness. These features provide quantitative indicators for assessing the vertical structure of vegetation and topographic stability. Time series features are extracted from long-term sensor and remote sensing data to capture trends, periodicity, seasonality, abrupt changes, and rates of change, revealing the dynamic evolution patterns of ecosystems. Statistical features are used to calculate the mean, variance, skewness, kurtosis, and other statistical measures of various monitoring data within a local area to reflect the aggregation characteristics and spatial heterogeneity of ecological parameters within the region.

[0109] The deep multimodal data fusion unit is the core innovation of this module. It employs a deep learning-based architecture to achieve deep fusion and information complementarity among multi-source heterogeneous data, generating high-dimensional, semantically rich comprehensive ecological feature representations. Specifically, this unit constructs a multi-branch encoder network with multiple parallel branches, each specializing in processing data of a specific modality. For example, one branch uses a 2D convolutional neural network (CNN) with a ResNet-50 residual network as its backbone to process remote sensing optical images and extract spatial context features; another branch uses a 3D convolutional neural network to process time-series SAR images and capture spatiotemporal dynamic textures; yet another branch uses a Transformer encoder to process time-series sensor data to capture long-term dependencies in the data; and a third branch uses a PointNet++ network to process LiDAR point cloud data, directly extracting 3D vegetation structure and terrain features from irregular point clouds. Each branch independently extracts deep feature representations from its corresponding modality data. Following the multi-branch encoder, a cross-modal attention mechanism fusion layer is introduced. The attention mechanism allows information interaction and weight allocation between features from different modalities. Specifically, features from one modality can be used as queries, while features from other modalities serve as keys and values. By calculating attention weights, discriminative information from different modalities can be selectively aggregated. For example, a multi-head self-attention mechanism can capture long-range dependencies within a single modality, while a cross-attention mechanism can capture correlations between different modalities. For instance, the penetration features of SAR data can be used to enhance the estimation of biomass from optical data, thereby enhancing feature complementarity. Next, the multimodal feature vectors weighted and fused by the attention mechanism are concatenated to form a unified, high-dimensional fused feature vector. The concatenated feature vector is then further input into a fully connected network or a multilayer perceptron for nonlinear transformation. This network typically contains 3-5 hidden layers, each using the ReLU activation function, and uses dropout layers to prevent overfitting. The final output is a deep fused feature that comprehensively reflects multidimensional information about the ecosystem. For data with spatiotemporal correlations, this unit also employs a spatiotemporal graph convolutional network or a 3D CNN to fuse sensor data and remote sensing images from different time points and spatial locations as a spatiotemporal cube, capturing spatiotemporal dynamic evolution patterns. The fusion output is a unified, high-dimensional set of fused feature vectors. This set can comprehensively and accurately characterize the current state, spatial structure, and dynamic evolution trend of the ecosystem, providing high-confidence input data for subsequent ecosystem modeling and assessment. For example, for a 256x256 pixel region, after fusion, a 256x256xN-dimensional feature tensor can be obtained, where N represents the fused feature dimension.

[0110] Furthermore, in step S3, the ecosystem dynamic state modeling and assessment module specifically includes:

[0111] An ecological index calculation unit is used to calculate a series of quantitative ecological indicators based on the fusion features output in step S2. The indicators include vegetation ecological indicators, hydrological ecological indicators, soil ecological indicators, biodiversity indicators, and the ecosystem health index (EHI).

[0112] The ecosystem service function quantification unit is used to quantitatively assess the ecosystem service functions within the restoration area. These service functions include carbon sequestration and oxygen release, water conservation, soil retention, biodiversity maintenance, and air purification.

[0113] An ecosystem dynamic evolution model unit is used to construct and run ecosystem dynamic evolution prediction models to predict future trends of ecological indicators and identify potential risks. The models include time series prediction models, state-space models, and scenario simulation models.

[0114] It outputs real-time updated ecosystem health status assessment reports, quantitative results of various ecosystem service functions, prediction maps of future dynamic evolution of the ecosystem, and early warning information on potential risks.

[0115] In this embodiment, the ecological index calculation unit first calculates a series of quantified ecological indicators based on the fusion features output in step two. Vegetation ecological indicators include, but are not limited to, vegetation cover (VFC), calculated using a remote sensing image pixel decomposition model; leaf area index (LAI), calculated based on radiative transfer models such as the PROSAIL model; net primary productivity (NPP), calculated based on light use efficiency models such as CPEC or MODIS NPP product algorithms; gross primary productivity (GPP); and biomass. Hydrological ecological indicators include soil moisture index (SMI), derived from the fusion of microwave remote sensing data such as SMAP and ground sensor data; surface runoff coefficient, obtained through hydrological model simulation; evapotranspiration, calculated based on energy balance equations such as the SEBAL model; and groundwater level dynamics. Soil ecological indicators include soil organic matter content, soil erosion modulus, and soil fertility index. Biodiversity indicators cover habitat fragmentation index, species richness index, Shannon-Wiener diversity index, and Simpson diversity index. To provide a comprehensive quantification of the overall ecosystem health, this unit further calculates the ecosystem health index (EHI). EHI calculates by constructing a multi-factor comprehensive evaluation model. This model uses the analytic hierarchy process (AHP) to determine the weight of each ecological indicator and uses linear weighted sum or fuzzy comprehensive evaluation method to aggregate each indicator to form a single quantitative indicator that reflects the overall health status of the ecosystem.

[0116] The Ecosystem Service Function Quantification Unit quantifies the ecosystem service functions within the restoration area. Carbon sequestration and oxygen release function quantifies the ecosystem's carbon sink capacity using vegetation NPP and biomass data, combined with carbon storage coefficients; for example, the annual carbon sequestration per hectare can reach several tons. Water conservation function assesses the region's water conservation capacity using hydrological models, combined with data on precipitation, evapotranspiration, topography, soil type, and land use, outputting the average annual water conservation amount. Soil conservation function quantifies the ecosystem's soil conservation based on the RUSLE model or its improved versions, combined with vegetation cover, topographic slope, soil type, and rainfall erosivity factors; for example, the annual reduction in soil loss. Biodiversity maintenance function assesses the region's habitat quality and biodiversity maintenance capacity using habitat quality assessment models, combined with land use type, habitat threat factors, and biodiversity data, outputting a habitat quality index. Air purification function assesses the adsorption and retention capacity of vegetation for air pollutants such as PM2.5, SO2, and NOx based on vegetation leaf area index and meteorological data, quantifying its contribution to improving air quality.

[0117] The Ecosystem Dynamic Evolution Model Unit is used to construct and run ecosystem dynamic evolution prediction models to predict future trends of ecological indicators and identify potential risks. Time series prediction models employ deep learning-based Transformer models or Long Short-Term Memory (LSTM) networks. By learning the time-series patterns of historical ecological indicators, they predict their future trends. These models can capture complex nonlinear time dependencies and long-term trends. For example, an LSTM model trained on three years of historical data can control the mean absolute error (MAE) of its NDVI prediction for the next year to within 0.05. State-space models employ Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) algorithms. These algorithms model various ecosystem indicators as state variables and dynamically estimate and correct these state variables using real-time monitoring data, while quantifying the uncertainty of state estimates. Qualitative analysis enables real-time tracking and prediction of complex dynamic processes in ecosystems, particularly suitable for scenarios with significant sensor noise and model uncertainty. Scenario simulation models construct cellular automata or system dynamics models to simulate the evolution paths and spatial patterns of key ecosystem indicators under different climate change scenarios, human interventions, or restoration measures. The models can support predictions of future restoration effects and risk assessments; for example, simulating the reduction in soil erosion with a 20% increase in vegetation cover. The assessment results output as a real-time updated ecosystem health assessment report, quantitative results of various ecosystem service functions, a predicted map of future ecosystem dynamic evolution, and early warning information on potential risks.

[0118] Furthermore, in step S4, the ecological restoration planning and dynamic optimization module specifically includes:

[0119] The ecological restoration target setting unit is used to set specific, quantifiable, and temporally and spatially differentiated ecological restoration targets based on national and local ecological and environmental protection strategies, regional ecological baseline conditions, degree of degradation, and socio-economic development needs.

[0120] The spatial zoning and priority ranking unit is used to utilize the spatial analysis technology of Geographic Information System (GIS) and combine it with the ecosystem health assessment results output in step S3 to conduct refined ecological functional zoning of the restoration area and determine the restoration priority and appropriate restoration measures for each zoning.

[0121] A smart repair measure recommendation unit is used to intelligently recommend a combination of targeted repair measures based on the spatial partitioning results and repair priorities; and

[0122] The dynamic adjustment and optimization strategy unit is used to adaptively adjust and optimize the implemented repair plan based on the real-time monitoring feedback of step S5.

[0123] The module outputs an initial ecological restoration plan and a dynamic adjustment suggestion report generated based on real-time monitoring feedback.

[0124] In this embodiment, the restoration target setting unit sets specific, quantifiable, and temporally and spatially differentiated ecological restoration targets based on national and local ecological environmental protection strategies, regional ecological baseline conditions, degradation levels, and socio-economic development needs. These targets are determined through consultation with stakeholders and expert review and are incorporated into the planning benchmark document. For example, in an ecological restoration project in the upper reaches of a river basin, targets might include: increasing vegetation coverage to over 85% within 3 years in the core restoration area; increasing soil organic matter content by an average of 0.3% annually over 5 years; achieving Class III surface water quality standards for major tributaries within 2 years; and increasing the diversity index of specific indicator bird species by 10% within 3 years.

[0125] Spatial zoning and priority ranking units utilize Geographic Information System (GIS) spatial analysis technology, combined with various geospatial data such as ecosystem health assessment results, topographic data, soil type, water system distribution, land use status, and potential ecological risks output from step three, to conduct refined ecological functional zoning of the restoration area. Functional zoning employs multi-scale segmentation algorithms and fuzzy clustering algorithms to divide the area, identifying sub-regions with different degrees of degradation, different dominant ecological functions, and different restoration potentials. For each sub-region, a multi-criteria decision analysis (MCDA) method, such as the Analytic Hierarchy Process (AHP) or prospect theory, is used to comprehensively consider multiple criteria, including the degree of ecological degradation, restoration difficulty, ecological benefit potential, socio-economic benefits, and implementation costs, to determine the restoration priority and appropriate restoration measures for each zone. For example, in steep slope areas with severe soil erosion, the restoration priority is highest, and a combination of engineering and biological measures may be recommended; while in areas with degraded water conservation functions, vegetation restoration and soil and water conservation measures are given priority.

[0126] The intelligent recommendation unit for restoration measures intelligently recommends combinations of targeted restoration measures based on spatial partitioning results and restoration priorities. First, a comprehensive knowledge base of ecological restoration technologies is established, containing detailed descriptions of various restoration measures, applicable environmental conditions, expected ecological responses, implementation costs, cycles, and risks. For example, the knowledge base might include the measure of "planting aquatic vegetation," applicable to the early stages of eutrophication in water bodies at depths of 0.5-2 meters, with expected ecological responses of water purification and increased aquatic habitat, a cost of X yuan / square meter, a cycle of 1-2 years, and the risk of invasive alien species. Based on the ecological characteristics, degradation type, and restoration goals of the current partition, suitable restoration technologies are matched through rule-based reasoning or an expert system. Further, a multi-objective optimization algorithm is employed. The algorithm uses multiple objective functions—maximizing ecological benefits, minimizing total restoration costs, and minimizing implementation risks—to search for the optimal combination of restoration measures on the Pareto front, generating a series of optimal solutions that balance different objectives for decision-makers to choose from. For example, for a watershed, NSGA- II can generate a set of solutions, one of which may have lower costs but moderate ecological benefits, and another with higher costs but significant ecological benefits. Decision-makers can choose according to the actual situation. More advancedly, this invention introduces a reinforcement learning decision-making framework. The state of the ecosystem in different partitions and time steps is defined as the state space of the RL environment, feasible remediation measures are defined as the action space of the RL agent, and the improvement of ecological health index, the increase of ecosystem service functions, and resource consumption are defined as the reward function. By training a deep Q-network (DQN), a policy gradient algorithm, or an Actor-Critic algorithm, the RL agent can learn an optimal policy. This policy dynamically selects and executes the optimal combination of remediation measures based on the real-time state of the ecosystem to maximize long-term cumulative rewards, thereby achieving adaptive decision-making for remediation solutions. Its advantage lies in its ability to handle complex time-varying environments and delayed reward problems.

[0127] The schedule and resource allocation optimization unit develops detailed implementation schedules and resource allocation plans for the recommended remediation solutions. Critical path analysis, using the Critical Path Method (CPM) or Project Evaluation and Review Technique (PERT), analyzes the logical dependencies and estimates the time for each remediation task, identifies the critical paths affecting the overall project duration, and optimizes task arrangement to ensure on-time project completion. Resource linear programming or integer programming models, with the objective function of minimizing the consumption of human, material, and financial resources, optimize the allocation of various resources while meeting the remediation task requirements and time constraints. For example, in afforestation tasks, it optimizes the procurement quantity and planting time of seedlings of different tree species to maximize survival rate while minimizing transportation and labor costs.

[0128] The dynamic adjustment and optimization strategy unit adaptively adjusts and optimizes the implemented restoration plan based on the real-time monitoring feedback from step five. When the deviation between the real-time monitoring data and the expected targets or intermediate milestones set in the planning scheme exceeds a preset threshold, the system automatically triggers the dynamic adjustment process. The threshold can be dynamically set based on historical data, expert experience, or the uncertainty range of model predictions. For example, if the NDVI value of vegetation in a certain area is lower than 20% of the expected growth curve for two consecutive weeks, an early warning is triggered. Scenario simulation and risk assessment utilize the ecosystem dynamic evolution model from step three to simulate and pre-assess the ecological response, economic costs, and potential risks of different adjustment schemes, generating multiple alternative adjustment scenario reports to support decision-makers in selecting the optimal adjustment strategy. Adaptive learning and strategy updates continuously collect and analyze historical restoration case data and real-time monitoring feedback data. Through machine learning models, the system continuously learns and updates the complex causal relationship model between restoration measures and ecological responses, thereby optimizing the recommended restoration measures strategy and planning adjustment logic, improving the system's adaptability and robustness. Human-computer interaction decision support provides an intuitive visual interface and decision support tools, allowing managers and experts to review, revise, and confirm the adjustment suggestions generated by the system, achieving human-computer collaborative decision-making. The planning output consists of an initial ecological restoration plan and a dynamic adjustment suggestion report generated based on real-time monitoring feedback. The report includes the reasons for the adjustment, the content of the adjustment, the expected effects, and the potential risks.

[0129] Furthermore, referring to Figure 3 The intelligent recommendation unit for repair measures specifically includes:

[0130] A comprehensive knowledge base for ecological restoration technology is established by matching the knowledge base with the rule base. The knowledge base includes detailed descriptions of various restoration measures, applicable environmental conditions, expected ecological responses, implementation costs, cycles and risks. Based on the ecological characteristics, degradation types and restoration goals of the current zone, appropriate restoration technologies are matched through rule reasoning or expert systems.

[0131] A multi-objective optimization algorithm is configured, employing the multi-objective genetic algorithm MOGA. With multiple objective functions, such as maximizing ecological benefits, minimizing total restoration costs, and minimizing implementation risks, the algorithm searches for the optimal combination of restoration measures on the Pareto front, generating a series of optimal solutions that balance different objectives for decision-makers to choose from.

[0132] The reinforcement learning decision-making framework defines the state of the ecosystem in different partitions and time steps as the state space of the RL environment, the feasible remediation measures as the action space of the RL agent, and the improvement of the ecological health index, the increase of ecosystem service functions, and resource consumption as the reward function. By training a deep Q-network (DQN), a policy gradient algorithm, or an Actor-Critic algorithm, the RL agent can learn an optimal policy and dynamically select and execute the optimal combination of remediation measures based on the real-time state of the ecosystem.

[0133] Furthermore, in step S5, the real-time monitoring and effect evaluation module specifically includes:

[0134] The continuous data stream management unit is used to establish a real-time data processing pipeline based on big data stream processing technology to continuously acquire, clean, integrate and standardize multi-source heterogeneous monitoring data from ground sensor networks, UAV aerial photography and remote sensing platforms.

[0135] The key ecological parameter extraction and trend analysis unit is used to extract and analyze changes in key ecological parameters from real-time data streams. These changes include real-time parameter extraction, time series trend analysis, and anomaly detection and mutation identification.

[0136] The restoration effect quantitative evaluation unit is used to conduct multi-dimensional and quantitative evaluation of the effects in the ecological restoration process. The evaluation includes baseline comparison evaluation, target achievement evaluation, and comprehensive ecological benefit accounting.

[0137] The deviation warning and reporting unit is used to generate deviation warnings and provide visual reports based on the evaluation results. The warnings and reports include a multi-level warning mechanism, warning information push, and visual report generation.

[0138] The module outputs real-time ecological restoration progress reports, key parameter trend analysis charts, quantitative assessment reports of restoration effects, and multi-level deviation early warning notifications.

[0139] In this embodiment, the continuous data stream management unit establishes a real-time data processing pipeline based on large data stream processing technology. This pipeline is used to continuously acquire, clean, integrate, and standardize multi-source heterogeneous monitoring data from ground sensor networks and remote sensing platforms. This data pipeline features low latency and high throughput, ensuring real-time data availability and supporting stable processing of large-scale concurrent data streams.

[0140] The key ecological parameter extraction and trend analysis unit extracts and analyzes changes in key ecological parameters from real-time data streams. Real-time parameter extraction utilizes a pre-trained deep learning model to extract macroscopic spatial parameters such as vegetation cover, water area, bare soil area, soil erosion patches, and pest and disease distribution areas from real-time remote sensing or UAV imagery with high precision, achieving a segmentation accuracy of over 0.9. Time series trend analysis performs real-time time series analysis on data from ground sensors, employing exponential smoothing, moving average, or seasonal decomposition methods to identify short-term fluctuations, long-term trends, seasonal patterns, and potential periodicity. Anomaly detection and mutation identification use statistical methods or machine learning algorithms to perform real-time anomaly detection on various parameters, identifying monitoring values ​​deviating from normal ranges or key events indicating mutations in the ecosystem, such as a sudden drop in water pH or an abnormal decrease in vegetation NDVI values.

[0141] The quantitative assessment unit for restoration effectiveness conducts multi-dimensional and quantitative evaluations of the effects of ecological restoration. The baseline comparison assessment system compares real-time monitored ecological indicators with baseline data prior to restoration projects, quantifying restoration progress and improvement. Comparative analyses include, but are not limited to, percentage increases in vegetation cover (e.g., from 60% to 78%, an increase of 18%), increases in soil organic matter content (e.g., from 1.5% to 2.1%, an increase of 0.6 percentage points), improvements in water quality indicators (e.g., dissolved oxygen from 3 mg / L to 6 mg / L, an increase of 3 mg / L), changes in biodiversity index, and improvements in the Ecosystem Health Index (EHI). The target achievement assessment compares real-time monitoring data with the phased and final targets set in the planning scheme, calculates the target achievement rate for each indicator, and identifies gaps and non-compliance areas during the restoration process. The comprehensive ecological benefit accounting adopts an ecosystem service value assessment model to qualitatively and quantitatively calculate the ecological benefits brought by restoration, such as increased water conservation, biodiversity protection, soil retention, carbon sequestration, air purification, and landscape aesthetic value, and provides a corresponding economic value assessment report. For example, through quantitative calculation, a certain restoration project can increase water conservation by 1 million cubic meters per year, bringing economic value of 2 million yuan.

[0142] The deviation warning and reporting unit generates deviation warnings and provides visual reports based on the assessment results. A multi-level warning mechanism sets multiple warning thresholds. When the real-time values ​​or trends of key ecological parameters deviate significantly from the expected restoration trajectory and exceed these preset thresholds, the system automatically triggers the corresponding level of warning. For example, an NDVI value deviating 5%-10% from the expected curve is a mild warning, 10%-20% is a moderate warning, and over 20% is a severe warning. Thresholds can be dynamically adjusted based on historical data statistical analysis, expert experience, or the uncertainty range predicted by the dynamic model in step three. Warning information is pushed in real-time to the ecological restoration planning and dynamic optimization module via a unified application programming interface (API) or message bus, triggering its dynamic adjustment process. Simultaneously, information can be sent to management personnel via SMS, email, or mobile applications to ensure timely delivery. Visual report generation includes dynamic charts of various monitoring parameters, spatial distribution maps of restoration progress, highlighted maps of abnormal areas, and detailed textual reports. The reports intuitively display restoration effects, potential problem areas, and warning information, supporting management personnel in quickly understanding the restoration site status and responding accordingly. The assessment results are output as a real-time ecological restoration progress report, a key parameter trend analysis chart, a quantitative assessment report of restoration effects, and a multi-level deviation early warning notification. The output data will serve as the basis for decision-making in the ecological restoration planning and dynamic optimization modules to adjust the scheme.

[0143] Furthermore, the quantitative evaluation unit for the repair effect specifically includes:

[0144] Baseline comparison assessment involves comparing and analyzing various ecological indicators obtained from real-time monitoring with baseline data before the implementation of restoration projects from multiple dimensions to quantify restoration progress and improvement. The comparison analysis includes the percentage increase in vegetation coverage, the increase in soil organic matter content, the improvement in water quality indicators, changes in biodiversity index, and the improvement in the ecosystem health index (EHI).

[0145] The goal achievement assessment compares real-time monitoring data with the phased and final goals set in the planning scheme, calculates the goal achievement rate of each indicator, and identifies gaps and unmet areas in the repair process.

[0146] The comprehensive ecological benefit accounting adopts an ecosystem service value assessment model to qualitatively and quantitatively calculate the ecological benefits brought about by restoration, including water conservation increment, biodiversity protection effectiveness, soil retention, carbon sequestration increment, air purification benefits, and landscape aesthetic value, and provides a corresponding economic value assessment report.

[0147] In this embodiment, the remote sensing image data acquired by the remote sensing data acquisition unit includes:

[0148] High-resolution optical remote sensing satellites provide multispectral data with features such as 10-meter spatial resolution and coverage of the visible to near-infrared bands. This data is used to extract information on land surface vegetation cover, leaf area index (LAI), normalized difference vegetation index (NDVI), surface water distribution, and land use type.

[0149] The radar imagery data provided by the Synthetic Aperture Radar (SAR) satellite has the characteristics of C-band or L-band and VV or VH polarization, which can be used to penetrate clouds and some vegetation to obtain information on soil moisture, vegetation biomass and surface roughness.

[0150] LiDAR data includes point cloud data acquired by airborne or spaceborne lidar. Point cloud data has high density characteristics and is used to generate high-precision digital elevation models (DEM), digital surface models (DSM), vegetation height, canopy structure, and forest biomass three-dimensional information.

[0151] The sensor nodes deployed in the ground sensor network acquisition unit include:

[0152] Soil sensors are used to monitor soil moisture, soil temperature, soil pH, soil electrical conductivity, and the content of key nutrients such as nitrogen, phosphorus, and potassium in the soil in real time.

[0153] Water quality sensors are used to monitor water quality parameters such as pH, dissolved oxygen (DO), conductivity, turbidity, ammonia nitrogen, total phosphorus, and chlorophyll a in real time.

[0154] Biological monitoring sensors, including infrared camera traps, are used to record wildlife activity and species diversity;

[0155] Acoustic sensors are used to monitor the vocal spectra of birds and amphibians and assess their acoustic health.

[0156] The environmental DNAe sampling unit is used to collect water or soil samples and analyze the composition and diversity of biological communities through gene sequencing technology.

[0157] Climate and meteorological stations are used to monitor local meteorological parameters such as temperature, relative humidity, precipitation, wind speed and direction, and solar radiation in real time.

[0158] Ground-based sensor networks transmit monitoring data to a data aggregation center via wireless communication technology.

[0159] The deep multimodal data fusion unit specifically includes:

[0160] Multi-branch encoder networks construct a deep convolutional neural network (CNN) or recurrent neural network (RNN) structure with multiple parallel branches. Each branch is responsible for processing data of a specific modality and independently extracts deep feature representations from its corresponding modality data.

[0161] A cross-modal attention mechanism fusion layer is introduced after the multi-branch encoder, which allows information interaction and weight allocation between features from different modalities. By calculating attention weights, discriminative information from different modalities is selectively aggregated.

[0162] The feature concatenation and fusion network concatenates the multimodal feature vectors after weighted fusion through an attention mechanism to form a unified, high-dimensional fusion feature vector. The concatenated feature vector is then input into a fully connected network or a multilayer perceptron for nonlinear transformation, ultimately outputting comprehensive ecological features.

[0163] Multi-branch encoder networks include:

[0164] A branch that uses 2D CNN to process remotely sensed optical images;

[0165] A branch of processing temporal SAR images using 3D CNN;

[0166] A branch that uses a Transformer encoder to process time-series sensor data;

[0167] A branch that uses point cloud neural networks to process LiDAR point cloud data.

[0168] The Ecosystem Health Index (EHI) calculated by the Ecological Indicator Calculation Unit is calculated by constructing a multi-factor comprehensive evaluation model. The model uses the Analytic Hierarchy Process (AHP) to determine the weights of each ecological indicator and uses linear weighted sum or fuzzy comprehensive evaluation methods to aggregate the indicators.

[0169] According to a second embodiment of the present invention, referring to Figure 4 This invention claims protection for an ecological restoration planning and monitoring system based on multi-source data technology, comprising:

[0170] A multi-source heterogeneous data acquisition and preprocessing module, which is used to acquire and standardize various types of ecological monitoring data;

[0171] A multimodal data fusion and feature extraction module, the module being used to perform deep fusion and high-dimensional feature extraction on the heterogeneous data;

[0172] An ecosystem dynamic state modeling and assessment module is used to establish an ecosystem dynamic model and assess its health status and service functions based on the fusion features;

[0173] An ecological restoration planning and dynamic optimization module is used to generate an initial ecological restoration plan based on the assessment results and to make adaptive adjustments based on real-time monitoring feedback.

[0174] The real-time monitoring and effect evaluation module is used to continuously acquire real-time data during the ecological restoration process, evaluate the restoration progress and effect, and feed the evaluation results back to the ecological restoration planning and dynamic optimization module.

[0175] The system also includes a data bus for enabling seamless data transmission and collaborative operation between modules;

[0176] The system also includes a unified geospatial database for storing all processed geospatial data, metadata, and planning schemes; and

[0177] The system also includes a visualization and decision support platform, which provides an intuitive user interface for displaying real-time monitoring data, evaluation results, planning schemes and adjustment suggestions, supporting managers to make human-machine collaborative decisions.

[0178] The aforementioned ecological restoration planning and monitoring system based on multi-source data technology is used to execute the aforementioned ecological restoration planning and monitoring method based on multi-source data technology.

[0179] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0180] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0181] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A method for monitoring ecological restoration planning based on multi-source data technology, characterized in that, Includes the following steps: S1, Construct a multi-source heterogeneous data acquisition and preprocessing module to acquire and standardize various types of ecological monitoring data, including remote sensing data, UAV aerial photography data, ground sensor network data, and auxiliary geographic information and socio-economic data; S2, Construct a multimodal data fusion and feature extraction module to perform deep fusion and high-dimensional feature extraction on the heterogeneous data obtained in step S1 to generate a comprehensive ecological feature representation; S3, Construct an ecosystem dynamic state modeling and assessment module, based on the comprehensive ecological characteristics extracted in step S2, establish an ecosystem dynamic model and assess its health status and service functions; S4. Construct an ecological restoration planning and dynamic optimization module. Based on the evaluation results of step S3, generate an initial ecological restoration plan and make adaptive adjustments based on real-time monitoring feedback. S5. Construct a real-time monitoring and effect evaluation module to continuously acquire real-time data during the ecological restoration process, evaluate the restoration progress and effect, and feed the evaluation results back to the ecological restoration planning and dynamic optimization module to form a closed-loop management. In step S4, the ecological restoration planning and dynamic optimization module specifically includes: The ecological restoration target setting unit is used to set ecological restoration targets with temporal and spatial differences based on the regional ecological baseline conditions and degree of degradation. The spatial zoning and priority ranking unit is used to utilize the spatial analysis technology of Geographic Information System (GIS) and combine it with the ecosystem health assessment results output in step S3 to conduct refined ecological functional zoning of the restoration area and determine the restoration priority and appropriate restoration measures for each zoning. A smart repair measure recommendation unit is used to intelligently recommend a combination of targeted repair measures based on the spatial partitioning results and repair priorities; and The dynamic adjustment and optimization strategy unit is used to adaptively adjust and optimize the implemented repair plan based on the real-time monitoring feedback of step S5. The module outputs an initial ecological restoration plan and a dynamic adjustment suggestion report generated based on real-time monitoring feedback. The intelligent recommendation unit for remedial measures specifically includes: A comprehensive knowledge base for ecological restoration technology is established by matching the knowledge base with the rule base. The knowledge base includes detailed descriptions of various restoration measures, applicable environmental conditions, expected ecological responses, implementation costs, cycles and risks. Based on the ecological characteristics, degradation types and restoration goals of the current zone, appropriate restoration technologies are matched through rule reasoning or expert systems. The multi-objective optimization algorithm employs the multi-objective genetic algorithm MOGA, which uses multiple objective functions to maximize ecological benefits, minimize total restoration costs, and minimize implementation risks. It searches for the optimal combination of restoration measures on the Pareto front, generating a series of optimal solutions that balance different objectives for decision-makers to choose from. The reinforcement learning decision-making framework defines the state of the ecosystem in different partitions and time steps as the state space of the RL environment, the feasible remediation measures as the action space of the RL agent, and the improvement of the ecological health index, the increase of ecosystem service functions, and resource consumption as the reward function. By training a deep Q-network (DQN), a policy gradient algorithm, or an Actor-Critic algorithm, the RL agent can learn an optimal policy and dynamically select and execute the optimal combination of remediation measures based on the real-time state of the ecosystem.

2. The ecological restoration planning and monitoring method based on multi-source data technology according to claim 1, characterized in that, In step S1, the multi-source heterogeneous data acquisition and preprocessing module specifically includes: The remote sensing data acquisition unit is used to acquire remote sensing image data with different spatial resolutions, spectral characteristics and temporal resolutions, including multispectral data provided by high-resolution optical remote sensing satellites, radar image data provided by synthetic aperture radar (SAR) satellites and lidar (LiDAR) data. The UAV aerial photography data acquisition unit is used to carry multiple types of sensors to acquire ultra-high resolution near-surface data. The sensor types include high-resolution visible light cameras, multispectral or hyperspectral cameras, thermal infrared cameras, and UAV-borne lidar systems. The ground sensor network acquisition unit is used to deploy distributed, real-time monitoring sensor nodes in the restoration area. The sensor nodes include soil sensors, water quality sensors, biological monitoring sensors, and climate and meteorological stations. The auxiliary geographic information and socio-economic data acquisition unit is used to integrate high-precision digital elevation model (DEM), land use cover type map, administrative division boundaries, water system distribution, road network, population density distribution map, regional economic activity data, and relevant policy and regulatory documents; The data preprocessing unit is used to perform data format standardization and coordination, geometric correction and registration, radiometric correction and atmospheric correction, noise removal and cloud / shadow removal, missing data imputation, and data quality assessment on the multi-source heterogeneous data collected above.

3. The method for monitoring ecological restoration planning based on multi-source data technology according to claim 2, characterized in that, The preprocessing operations performed by the data preprocessing unit include: Data format standardization and harmonization unify data from different sources and in different formats into a standardized geospatial data format, and unify spatial reference systems and projection methods; Geometric correction and registration: Perform geometric fine correction on remote sensing imagery and UAV imagery to eliminate geometric distortions caused by terrain, sensor attitude and Earth curvature, and perform spatial registration with high-precision geographic base maps; Radiometric correction and atmospheric correction: Radiometric correction is performed on optical remote sensing images to convert the digital quantization values ​​recorded by the sensor into surface reflectance, and atmospheric correction is performed to eliminate the effects of atmospheric scattering and absorption. Noise removal and cloud / shadow removal are achieved by using image filtering algorithms to remove image noise. For optical images, cloud removal and shadow removal algorithms based on time series analysis or spectral features are used to restore the surface information covered by clouds. For missing data imputation, data reconstruction is performed using time series prediction models or spatial interpolation algorithms to address the lack of monitoring data due to sensor failure or external interference.

4. The method for monitoring ecological restoration planning based on multi-source data technology according to claim 1, characterized in that, In step S2, the multimodal data fusion and feature extraction module specifically includes: A data heterogeneity processing unit is used to overcome the differences in spatial resolution, temporal frequency, spectral characteristics and data structure of multi-source data. The processing includes spatial resolution unification, timestamp alignment and feature dimension unification. The multi-level feature extraction unit is used to extract multi-dimensional and multi-scale ecological features from preprocessed data, including spectral features, texture features, geometric and morphological features, time series features, and statistical features. A deep multimodal data fusion unit, which adopts a deep learning-based architecture, realizes deep fusion and information complementarity among multi-source heterogeneous data to generate a high-dimensional comprehensive ecological feature representation; The output of the fusion and feature extraction module is a unified, high-dimensional set of fused feature vectors.

5. The method for monitoring ecological restoration planning based on multi-source data technology according to claim 1, characterized in that, In step S3, the ecosystem dynamic state modeling and assessment module specifically includes: An ecological index calculation unit is used to calculate a series of quantitative ecological indicators based on the fusion features output in step S2. The indicators include vegetation ecological indicators, hydrological ecological indicators, soil ecological indicators, biodiversity indicators, and the ecosystem health index (EHI). The ecosystem service function quantification unit is used to quantitatively assess the ecosystem service functions within the restoration area. These service functions include carbon sequestration and oxygen release, water conservation, soil retention, biodiversity maintenance, and air purification. An ecosystem dynamic evolution model unit is used to construct and run ecosystem dynamic evolution prediction models to predict future trends of ecological indicators and identify potential risks. The models include time series prediction models, state-space models, and scenario simulation models. It outputs real-time updated ecosystem health status assessment reports, quantitative results of various ecosystem service functions, prediction maps of future dynamic evolution of the ecosystem, and early warning information on potential risks.

6. The method for monitoring ecological restoration planning based on multi-source data technology according to claim 1, characterized in that, In step S5, the real-time monitoring and effect evaluation module specifically includes: The continuous data stream management unit is used to establish a real-time data processing pipeline based on big data stream processing technology to continuously acquire, clean, integrate and standardize multi-source heterogeneous monitoring data from ground sensor networks, UAV aerial photography and remote sensing platforms. The key ecological parameter extraction and trend analysis unit is used to extract and analyze changes in key ecological parameters from real-time data streams. These changes include real-time parameter extraction, time series trend analysis, and anomaly detection and mutation identification. The restoration effect quantitative evaluation unit is used to conduct multi-dimensional and quantitative evaluation of the effects in the ecological restoration process. The evaluation includes baseline comparison evaluation, target achievement evaluation, and comprehensive ecological benefit accounting. The deviation warning and reporting unit is used to generate deviation warnings and provide visual reports based on the evaluation results. The warnings and reports include a multi-level warning mechanism, warning information push, and visual report generation. The module outputs real-time ecological restoration progress reports, key parameter trend analysis charts, quantitative assessment reports of restoration effects, and multi-level deviation early warning notifications.

7. The method for monitoring ecological restoration planning based on multi-source data technology according to claim 6, characterized in that, The quantitative evaluation unit for the repair effect specifically includes: Baseline comparison assessment involves comparing and analyzing various ecological indicators obtained from real-time monitoring with baseline data before the implementation of restoration projects from multiple dimensions to quantify restoration progress and improvement. The comparison analysis includes the percentage increase in vegetation coverage, the increase in soil organic matter content, the improvement in water quality indicators, changes in biodiversity index, and the improvement in the ecosystem health index (EHI). The goal achievement assessment compares real-time monitoring data with the phased and final goals set in the planning scheme, calculates the goal achievement rate of each indicator, and identifies gaps and unmet areas in the repair process. The comprehensive ecological benefit accounting adopts an ecosystem service value assessment model to qualitatively and quantitatively calculate the ecological benefits brought about by restoration, including water conservation increment, biodiversity protection effectiveness, soil retention, carbon sequestration increment, air purification benefits, and landscape aesthetic value, and provides a corresponding economic value assessment report.

8. An ecological restoration planning and monitoring system based on multi-source data technology, used to execute the ecological restoration planning and monitoring method based on multi-source data technology as described in any one of claims 1-7, characterized in that, include: A multi-source heterogeneous data acquisition and preprocessing module, which is used to acquire and standardize various types of ecological monitoring data; A multimodal data fusion and feature extraction module, the module being used to perform deep fusion and high-dimensional feature extraction on the heterogeneous data; An ecosystem dynamic state modeling and assessment module is used to establish an ecosystem dynamic model based on fusion characteristics and assess its health status and service functions; An ecological restoration planning and dynamic optimization module is used to generate an initial ecological restoration plan based on the assessment results and to make adaptive adjustments based on real-time monitoring feedback. The real-time monitoring and effect evaluation module is used to continuously acquire real-time data during the ecological restoration process, evaluate the restoration progress and effect, and feed the evaluation results back to the ecological restoration planning and dynamic optimization module. The system also includes a data bus for enabling seamless data transmission and collaborative operation between modules; The system also includes a unified geospatial database for storing all processed geospatial data, metadata, and planning schemes; and The system also includes a visualization and decision support platform, which provides an intuitive user interface for displaying real-time monitoring data, evaluation results, planning schemes and adjustment suggestions, supporting managers in making human-machine collaborative decisions.

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