Ecological system restoration demand rapid division system and method

Abstract

The invention discloses a system and a method for quickly dividing ecological system restoration requirements, belongs to the technical field of ecological system restoration, and remarkably improves the efficiency and the accuracy of ecological problem identification. By integrating satellite remote sensing, ground monitoring and historical data, key problem areas such as severe water and soil loss areas and vegetation degradation zones can be quickly locked, and restoration priorities are automatically divided. Traditional manual evaluation needs an analysis process of several months, the system can be completed in several days, subjective judgment errors are avoided, and the scientificity of degradation grade judgment and restoration scheme matching is ensured. Besides, the dynamic tracking module monitors the restoration effect in real time and adjusts engineering parameters in time, the problem of resource waste caused by traditional'one-time treatment 'is solved, the integrity of the ecological barrier is guaranteed through the multi-dimensional balance mechanism, development space is reserved for village revitalization, and bi-directional improvement of ecological benefits and economic and social benefits is truly achieved.

Description

Technical Field

[0001] This invention belongs to the field of ecosystem restoration technology, specifically a system and method for rapidly classifying ecosystem restoration needs. Background Technology

[0002] Ecosystem restoration planning refers to a systematic and scientific set of action plans and strategies developed to restore or improve damaged, degraded, or dysfunctional ecosystems. Based on a thorough understanding of the current state, historical evolution, causes of damage, and ecological processes and functions of the damaged ecosystem, this plan clearly defines restoration goals, scope, and priorities. It typically includes detailed investigation and assessment, selection and design of restoration technologies, implementation steps, resource requirement budgeting, expected outcome prediction, monitoring and evaluation mechanisms, and long-term maintenance strategies. The aim is to promote biodiversity restoration, ecosystem structure optimization, and enhanced ecological functions through comprehensive engineering, biological, and managerial measures, ultimately achieving ecosystem health, stability, and sustainability.

[0003] However, the main drawback of existing technologies is that they rely on manual field surveys and static data analysis, which leads to low efficiency in identifying ecological problems and susceptibility to subjective biases. Restoration plans often lag behind the actual degradation process. At the same time, they often focus on a single ecological indicator and ignore social factors such as population distribution and industrial needs, which can easily cause conflicts between restoration measures and local development. For example, they may excessively restrict economic activities or ignore people's livelihood needs, ultimately affecting the sustainability and operability of ecological governance. Summary of the Invention

[0004] The purpose of this invention is to provide a system and method for rapidly classifying ecosystem restoration needs in order to solve the problems mentioned above.

[0005] The technical solution adopted in this invention is as follows: a system and method for rapidly classifying ecosystem restoration needs, the method comprising the following steps:

[0006] S1: Based on high-resolution remote sensing images and geospatial data, the study area was divided into independent patch units with distinct ecological function characteristics using multi-scale segmentation technology;

[0007] S2: Combining the ecosystem service value assessment index system, a convolutional neural network is used to quantitatively score the core service functions of each patch unit, such as water conservation, carbon sequestration and oxygen release.

[0008] S3: By cross-validating historical disaster data with real-time ecological monitoring data, a damage index model is constructed to dynamically identify the ecological degradation level of patch units;

[0009] S4: Introduce community participatory survey data and overlay it with socio-economic factors such as population density and industrial development needs to generate a repair priority weight matrix;

[0010] S5: Integrating geospatial weighting algorithms and landscape connectivity analysis, automatically generating heat maps of ecological corridor restoration needs across patches in the GIS platform;

[0011] S6: Develop a degradation pattern matching engine based on transfer learning to quickly associate with historical repair case libraries and recommend the optimal engineering implementation path;

[0012] S7: Establish a dynamic tracking mechanism for restoration effectiveness and use UAV multispectral remote sensing and ground sensor networks to achieve intelligent calibration of the restoration process;

[0013] S8: Construct a digital twin system for full-domain ecological restoration, and generate adaptive optimization schemes for full life cycle restoration strategies through multi-scenario simulation and prediction.

[0014] In a preferred embodiment, in step S1, based on the data from the Third National Land and Resources Survey and satellite imagery, 0.5-meter high-resolution remote sensing images are used to divide the study area into independent ecological patch units through multi-scale segmentation technology. The minimum area threshold for patch division is 0.1 square kilometers, and the shape index is controlled between 0.7 and 0.9 to ensure that ecosystem types such as forests, grasslands, and wetlands are isolated patches. Data preprocessing requires a unified mathematical basis of the CGCS2000 National Geodetic Coordinate System and the 1985 National Height Datum to ensure spatial consistency between remote sensing images and vector data, and data stitching and coordinate correction are completed through the ArcGIS platform.

[0015] In a preferred embodiment, in step S2, a convolutional neural network model is constructed to score the ecosystem service functions of patch units. The input layer data includes parameters such as ecosystem type, net primary productivity, rainfall erosivity factor, and vegetation cover. The output layer generates quantitative scores of 0 to 100 points for core functions such as water conservation, carbon sequestration, and oxygen release. Model training uses historical remote sensing-derived net primary productivity data and measured runoff and rainfall from meteorological stations. Weight parameters are optimized using a backpropagation algorithm, and the scoring thresholds are strictly based on the functional grading standards in the "Forest Ecosystem Service Function Assessment Specification" and the "Wetland Ecosystem Service Assessment Specification."

[0016] In a preferred embodiment, step S3 integrates ten-year soil erosion modulus, vegetation cover change trends, and water quality monitoring data of Weichang County to construct a dynamic damage index model. The degradation level is classified into three levels: mild degradation (index ≤ 30), moderate degradation (30 < index ≤ 60), and severe degradation (index > 60). Thresholds are set based on historical soil erosion area statistics and the average measured values ​​from ecological monitoring stations. For example, severely degraded areas must meet the following criteria: soil erosion modulus exceeding 500 tons / km²·year or NDVI value decreasing by more than 15%.

[0017] In a preferred embodiment, in step S4, a restoration priority weight matrix is ​​generated using the entropy method, integrating socioeconomic factors such as population density, arable land ratio, and tourism revenue. The core water conservation area accounts for 30% of the weight, the key carbon sequestration and oxygen release area accounts for 25%, the ecotourism hotspot area accounts for 20%, and the remaining weight is dynamically allocated based on the geological disaster risk level. Data sources include agricultural water consumption, industrial water consumption, and visitor numbers to A-level scenic spots from the Weichang County Statistical Yearbook, ensuring that the weight calculation accurately matches local development needs.

[0018] In a preferred embodiment, in step S5, a minimum cumulative resistance model is applied in the GIS platform to generate a heat map of ecological corridor restoration. Parameter settings include a slope resistance coefficient of 0.1-0.5, and land use type resistance values ​​of 1 for forest, 2 for grassland, and 5 for bare land. The corridor width is set as a 200-meter buffer zone based on animal migration path analysis, and the landscape connectivity index threshold is required to be no less than 0.6. Key nodes are prioritized to connect ecological barrier areas such as the Saihanba Mechanical Forest Farm and the Luanhe River Upper Reaches National Nature Reserve.

[0019] In a preferred embodiment, in step S6, a transfer learning-driven degradation pattern matching engine is developed, and the training set integrates historical case data such as the Saihanba Forest Farm restoration project and the Luanhe Wetland restoration project. Input features include indicators such as NPP decline rate greater than 15% and soil loss exceeding 500 tons / km²·year. The engine automatically recommends optimal engineering parameters, such as an artificial afforestation density of 2000 trees / hectare and a coniferous-broadleaf mixed planting ratio of 6:4, and associates them with restoration schemes for similar areas in the "Ecosystem Service Value Assessment Study of Qinghai Province".

[0020] In a preferred embodiment, step S7 involves establishing a multi-source collaborative monitoring network to track the remediation effectiveness. Unmanned aerial vehicle (UAV) multispectral remote sensing covers the 450-900 nm band, and the ground sensor network is deployed at a density of 5 nodes per square kilometer, monitoring soil moisture hourly and updating vegetation index every 16 days in sync with MODIS data. A calibration mechanism requires data deviation thresholds to be controlled within ±5%, and abnormal data automatically triggers a manual verification process.

[0021] In a preferred embodiment, step S8 involves constructing a digital twin system for comprehensive ecological restoration, integrating a GEP assessment model and a spatiotemporal simulation engine. Key parameters include rainfall variation gradient ±10%, temperature fluctuation range ±2℃, and vegetation recovery cycle of 5–10 years. The system outputs three types of strategies based on multi-scenario predictions: extreme drought scenarios, economic development balance models, and natural disaster emergency plans. Decision-making is based on the spatiotemporal variation analysis results of the ecosystem service value of Weichang County, such as quantitative targets like increasing forest coverage to 75% or increasing flood control capacity by 30%.

[0022] In a preferred embodiment, an ecosystem restoration needs rapid classification system includes:

[0023] Multi-source data fusion and acquisition module: Integrates data from the third national land and resources survey of Hebei Province, satellite remote sensing imagery, meteorological monitoring data, and socio-economic statistics. It adopts the CGCS2000 coordinate system and the 1985 National Elevation Datum for unified spatial data, supporting multi-scale remote sensing image stitching and attribute field standardization. Core functions include data validity verification, spatial correction, and dynamic updating of the ecological base map.

[0024] The ecosystem service function quantitative assessment module, based on a convolutional neural network model, takes into account parameters such as net primary productivity, rainfall erosivity factor, and vegetation cover, and outputs quantitative scores for functions such as water conservation, carbon sequestration and oxygen release, and soil retention. Model training relies on historical remote sensing inversion data and measured values ​​from meteorological stations, and the scoring criteria strictly adhere to the grading thresholds outlined in the "Forest Ecosystem Service Function Assessment Specification."

[0025] The dynamic ecological degradation diagnosis module integrates indicators such as soil erosion modulus, NDVI change rate, and water pollutant concentration to construct a damage index model and automatically classify degradation levels as mild, moderate, and severe. Dynamic diagnosis relies on cross-validation of ten years of historical disaster data and real-time monitoring data, with threshold settings referencing statistics on soil erosion area and average values ​​from ecological stations in Weichang County.

[0026] Fix the priority decision module:

[0027] The weights of water conservation areas, carbon sequestration core areas, and ecotourism areas are calculated using the entropy method, and then overlaid with socioeconomic factors such as population density, arable land ratio, and tourism revenue to generate a restoration priority matrix. The weight allocation logic is embedded in local statistical yearbook data, supporting dynamic adjustment of geological disaster risk levels.

[0028] The intelligent planning module for ecological corridors automatically generates cross-patch restoration corridors in a GIS platform based on the minimum cumulative resistance model and landscape connectivity analysis. Parameter configurations include slope resistance coefficient, land use type resistance value, and animal migration route buffer zone. The connectivity index threshold is no less than 0.6, focusing on connecting key nodes such as the Saihanba Forest Farm and the upper reaches of the Luanhe River Nature Reserve.

[0029] The intelligent recommendation module for restoration solutions integrates a transfer learning engine and a historical restoration case library. After inputting features such as NPP decline rate and soil loss, it automatically matches cases such as the Saihanba Forest Farm afforestation project and the Luanhe Wetland restoration project, recommends the optimal engineering parameters such as afforestation density and mixed planting ratio, and associates restoration strategies for similar areas.

[0030] The remediation effectiveness tracking and calibration module deploys UAV multispectral remote sensing and a ground sensor network to monitor indicators such as vegetation index and soil moisture in real time. The calibration frequency is synchronized with MODIS data, and the deviation threshold is controlled within ±5%. Abnormal data triggers a manual verification process to ensure dynamic optimization of the remediation process.

[0031] Digital twin module for comprehensive ecological restoration:

[0032] A digital twin system driven by a GEP assessment model is constructed to simulate rainfall gradient changes, temperature fluctuations, and vegetation recovery cycles, outputting prediction schemes for multiple scenarios such as extreme drought, economic balance, and disaster emergency response. Decision support is based on the spatiotemporal variation analysis of ecosystem service value in Weichang County, with quantified targets such as increasing forest coverage to 75% or expanding flood control capacity by 30%.

[0033] Visualization and Decision Support Module: Provides a GIS map interface that dynamically displays ecological patch delineation, restoration heat maps, corridor planning, and performance tracking data. It supports multi-level access control, allowing government departments to access real-time reports on water conservation value distribution and degraded area statistics, while providing the public with an ecotourism hotspot search function.

[0034] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0035] 1. In this invention, the system significantly improves the efficiency and accuracy of ecological problem identification. By integrating satellite remote sensing, ground monitoring, and historical data, it can quickly pinpoint key problem areas such as severely eroded soil and vegetation degradation zones, and automatically prioritize restoration efforts. The traditional manual assessment process, which takes months, can be completed in just a few days, while avoiding subjective judgment errors and ensuring the scientific accuracy of degradation level assessments and restoration plan matching. Furthermore, the dynamic tracking module monitors restoration effects in real time and adjusts engineering parameters promptly, solving the resource waste problem caused by traditional "one-off treatments."

[0036] 2. In this invention, the restoration plan not only considers ecological indicators but also incorporates social factors such as population distribution, arable land demand, and tourism economy, avoiding the negative impact of a "one-size-fits-all" approach on people's livelihoods. For example, low-disruption restoration measures are prioritized in the core carbon sequestration area, while a strategy that emphasizes both landscape beautification and functional restoration is adopted in ecotourism hotspots. This multi-dimensional balancing mechanism not only ensures the integrity of the ecological barrier but also leaves room for rural revitalization, truly achieving a two-way improvement in ecological and economic and social benefits. Attached Figure Description

[0037] Figure 1 This is a schematic diagram illustrating the process of creating an ecosystem spatial distribution base map according to the present invention.

[0038] Figure 2This is a schematic diagram of the ecosystem service value assessment index system in this invention;

[0039] Figure 3 This is a schematic diagram of the overall method flow in this invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0041] Example:

[0042] Reference Figure 1-3 ,

[0043] A method for rapidly classifying ecosystem restoration needs, comprising the following steps:

[0044] S1: Based on high-resolution remote sensing images and geospatial data, the study area was divided into independent patch units with distinct ecological function characteristics using multi-scale segmentation technology;

[0045] S2: Combining the ecosystem service value assessment index system, a convolutional neural network is used to quantitatively score the core service functions of each patch unit, such as water conservation, carbon sequestration and oxygen release.

[0046] S3: By cross-validating historical disaster data with real-time ecological monitoring data, a damage index model is constructed to dynamically identify the ecological degradation level of patch units;

[0047] S4: Introduce community participatory survey data and overlay it with socio-economic factors such as population density and industrial development needs to generate a repair priority weight matrix;

[0048] S5: Integrating geospatial weighting algorithms and landscape connectivity analysis, automatically generating heat maps of ecological corridor restoration needs across patches in the GIS platform;

[0049] S6: Develop a degradation pattern matching engine based on transfer learning to quickly associate with historical repair case libraries and recommend the optimal engineering implementation path;

[0050] S7: Establish a dynamic tracking mechanism for restoration effectiveness and use UAV multispectral remote sensing and ground sensor networks to achieve intelligent calibration of the restoration process;

[0051] S8: Construct a digital twin system for full-domain ecological restoration, and generate adaptive optimization schemes for full life cycle restoration strategies through multi-scenario simulation and prediction.

[0052] In step S1, based on the data from the Third National Land Resources Survey and satellite imagery, 0.5-meter high-resolution remote sensing images are used to divide the study area into independent ecological patch units through multi-scale segmentation technology. The minimum area threshold for patch division is 0.1 square kilometers, and the shape index is controlled between 0.7 and 0.9 to ensure that ecosystem types such as forests, grasslands, and wetlands are isolated patches. Data preprocessing requires a unified mathematical basis of the CGCS2000 National Geodetic Coordinate System and the 1985 National Height Datum to ensure spatial consistency between remote sensing images and vector data. Data stitching and coordinate correction are then completed using the ArcGIS platform.

[0053] In step S2, a convolutional neural network model is constructed to score the ecosystem service functions of patch units. Input layer data includes parameters such as ecosystem type, net primary productivity, rainfall erosivity factor, and vegetation cover. The output layer generates quantitative scores from 0 to 100 for core functions such as water conservation, carbon sequestration, and oxygen release. Model training uses historical remote sensing-derived net primary productivity data and measured runoff and rainfall from meteorological stations. Weight parameters are optimized using a backpropagation algorithm, and the scoring thresholds are strictly based on the functional grading standards in the "Forest Ecosystem Service Function Assessment Specification" and the "Wetland Ecosystem Service Assessment Specification."

[0054] In step S3, a dynamic damage index model is constructed by integrating ten-year soil erosion modulus, vegetation cover change trends, and water quality monitoring data of Weichang County. The degradation level is classified into three levels: slight degradation (index ≤ 30), moderate degradation (30 < index ≤ 60), and severe degradation (index > 60). Thresholds are set based on historical soil erosion area statistics and the average measured values ​​from ecological monitoring stations. For example, severely degraded areas must meet the following criteria: soil erosion modulus exceeding 500 tons / km²·year or NDVI value decreasing by more than 15%.

[0055] In step S4, a restoration priority weight matrix is ​​generated using the entropy method, integrating socioeconomic factors such as population density, arable land ratio, and tourism revenue. The core water conservation area accounts for 30% of the weight, the key carbon sequestration and oxygen release area accounts for 25%, the ecotourism hotspot area accounts for 20%, and the remaining weights are dynamically allocated based on the geological disaster risk level. Data sources include agricultural water consumption, industrial water consumption, and visitor numbers to A-level scenic spots from the Weichang County Statistical Yearbook, ensuring that the weight calculation accurately matches local development needs.

[0056] In step S5, a minimum cumulative resistance model is applied in the GIS platform to generate a heat map of ecological corridor restoration. Parameter settings include a slope resistance coefficient of 0.1-0.5, and land use type resistance values ​​of 1 for forest, 2 for grassland, and 5 for bare land. The corridor width is set as a 200-meter buffer zone based on animal migration path analysis, and the landscape connectivity index threshold is required to be no less than 0.6. Key nodes are prioritized to connect ecological barrier areas such as the Saihanba Mechanical Forest Farm and the Luanhe River Upper Reaches National Nature Reserve.

[0057] In step S6, a degradation pattern matching engine driven by transfer learning is developed, and the training set integrates historical case data such as the Saihanba Forest Farm Restoration Project and the Luanhe Wetland Restoration Project. Input features include indicators such as NPP decline rate greater than 15% and soil loss exceeding 500 tons / km²·year. The engine automatically recommends optimal engineering parameters, such as an artificial afforestation density of 2000 trees / hectare and a coniferous-broadleaf mixed planting ratio of 6:4, and associates them with restoration schemes for similar areas in the "Ecosystem Service Value Assessment Study of Qinghai Province".

[0058] In step S7, a multi-source collaborative monitoring network is established to track the remediation effectiveness. UAV multispectral remote sensing covers the 450-900 nm band, and the ground sensor network is deployed at a density of 5 nodes per square kilometer, monitoring soil moisture hourly and vegetation index every 16 days in sync with MODIS data. A calibration mechanism requires data deviation thresholds to be controlled within ±5%, and abnormal data automatically triggers a manual verification process.

[0059] In step S8, a digital twin system for comprehensive ecological restoration is constructed, integrating the GEP assessment model and a spatiotemporal simulation engine. Core parameters include rainfall variation gradient ±10%, temperature fluctuation range ±2℃, and vegetation recovery cycle of 5–10 years. Multi-scenario predictions output three types of strategies: extreme drought scenarios, economic development balance models, and natural disaster emergency plans. Decision-making is based on the spatiotemporal variation analysis results of the ecosystem service value in Weichang County, such as quantitative targets like increasing forest coverage to 75% or increasing flood control capacity by 30%.

[0060] A rapid ecosystem restoration needs classification system, comprising:

[0061] Multi-source data fusion and acquisition module: Integrates data from the third national land and resources survey of Hebei Province, satellite remote sensing imagery, meteorological monitoring data, and socio-economic statistics. It adopts the CGCS2000 coordinate system and the 1985 National Elevation Datum for unified spatial data, supporting multi-scale remote sensing image stitching and attribute field standardization. Core functions include data validity verification, spatial correction, and dynamic updating of the ecological base map.

[0062] The ecosystem service function quantitative assessment module, based on a convolutional neural network model, takes into account parameters such as net primary productivity, rainfall erosivity factor, and vegetation cover, and outputs quantitative scores for functions such as water conservation, carbon sequestration and oxygen release, and soil retention. Model training relies on historical remote sensing inversion data and measured values ​​from meteorological stations, and the scoring criteria strictly adhere to the grading thresholds outlined in the "Forest Ecosystem Service Function Assessment Specification."

[0063] The dynamic ecological degradation diagnosis module integrates indicators such as soil erosion modulus, NDVI change rate, and water pollutant concentration to construct a damage index model and automatically classify degradation levels as mild, moderate, and severe. Dynamic diagnosis relies on cross-validation of ten years of historical disaster data and real-time monitoring data, with threshold settings referencing statistics on soil erosion area and average values ​​from ecological stations in Weichang County.

[0064] Fix the priority decision module:

[0065] The weights of water conservation areas, carbon sequestration core areas, and ecotourism areas are calculated using the entropy method, and then overlaid with socioeconomic factors such as population density, arable land ratio, and tourism revenue to generate a restoration priority matrix. The weight allocation logic is embedded in local statistical yearbook data, supporting dynamic adjustment of geological disaster risk levels.

[0066] The intelligent planning module for ecological corridors automatically generates cross-patch restoration corridors in a GIS platform based on the minimum cumulative resistance model and landscape connectivity analysis. Parameter configurations include slope resistance coefficient, land use type resistance value, and animal migration route buffer zone. The connectivity index threshold is no less than 0.6, focusing on connecting key nodes such as the Saihanba Forest Farm and the upper reaches of the Luanhe River Nature Reserve.

[0067] The intelligent recommendation module for restoration solutions integrates a transfer learning engine and a historical restoration case library. After inputting features such as NPP decline rate and soil loss, it automatically matches cases such as the Saihanba Forest Farm afforestation project and the Luanhe Wetland restoration project, recommends the optimal engineering parameters such as afforestation density and mixed planting ratio, and associates restoration strategies for similar areas.

[0068] The remediation effectiveness tracking and calibration module deploys UAV multispectral remote sensing and a ground sensor network to monitor indicators such as vegetation index and soil moisture in real time. The calibration frequency is synchronized with MODIS data, and the deviation threshold is controlled within ±5%. Abnormal data triggers a manual verification process to ensure dynamic optimization of the remediation process.

[0069] Digital twin module for comprehensive ecological restoration:

[0070] A digital twin system driven by a GEP assessment model is constructed to simulate rainfall gradient changes, temperature fluctuations, and vegetation recovery cycles, outputting prediction schemes for multiple scenarios such as extreme drought, economic balance, and disaster emergency response. Decision support is based on the spatiotemporal variation analysis of ecosystem service value in Weichang County, with quantified targets such as increasing forest coverage to 75% or expanding flood control capacity by 30%.

[0071] Visualization and Decision Support Module: Provides a GIS map interface that dynamically displays ecological patch delineation, restoration heat maps, corridor planning, and performance tracking data. It supports multi-level access control, allowing government departments to access real-time reports on water conservation value distribution and degraded area statistics, while providing the public with an ecotourism hotspot search function.

[0072] As can be seen from the above, this invention significantly improves the efficiency and accuracy of ecological problem identification. By integrating satellite remote sensing, ground monitoring, and historical data, it can quickly pinpoint key problem areas such as severely eroded soil and vegetation degradation zones, and automatically prioritize restoration efforts. The traditional manual assessment process, which takes months, can be completed in just a few days, while avoiding subjective judgment errors and ensuring the scientific accuracy of matching degradation levels with restoration plans. Furthermore, the dynamic tracking module monitors restoration effects in real time and adjusts engineering parameters promptly, solving the resource waste problem caused by traditional "one-off treatments."

[0073] In this invention, the restoration planning system not only considers ecological indicators but also incorporates social factors such as population distribution, arable land demand, and tourism economy, avoiding the negative impact of a "one-size-fits-all" approach on people's livelihoods. For example, low-disruption restoration measures are prioritized in the core carbon sequestration area, while a strategy that emphasizes both landscape beautification and functional restoration is adopted in ecotourism hotspots. This multi-dimensional balancing mechanism not only ensures the integrity of the ecological barrier but also leaves room for rural revitalization, truly achieving a two-way improvement in ecological and socio-economic benefits.

[0074] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0075] The foregoing description enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A rapid classification system and method for ecosystem restoration needs, characterized in that: The method includes the following steps: S1: Based on high-resolution remote sensing images and geospatial data, the study area was divided into independent patch units with distinct ecological function characteristics using multi-scale segmentation technology; S2: Combining the ecosystem service value assessment index system, a convolutional neural network is used to quantitatively score the core service functions of water conservation, carbon sequestration and oxygen release for each patch unit. S3: By cross-validating historical disaster data with real-time ecological monitoring data, a damage index model is constructed to dynamically identify the ecological degradation level of patch units; S4: Introduce community participatory survey data and overlay it with socio-economic factors such as population density and industrial development needs to generate a repair priority weight matrix; S5: Integrating geospatial weighting algorithms and landscape connectivity analysis, automatically generating heat maps of ecological corridor restoration needs across patches in the GIS platform; S6: Develop a degradation pattern matching engine based on transfer learning to quickly associate with historical repair case libraries and recommend the optimal engineering implementation path; S7: Establish a dynamic tracking mechanism for restoration effectiveness and use UAV multispectral remote sensing and ground sensor networks to achieve intelligent calibration of the restoration process; S8: Construct a digital twin system for full-domain ecological restoration, and generate adaptive optimization schemes for full life cycle restoration strategies through multi-scenario simulation and prediction.

2. The system and method for rapid classification of ecosystem restoration needs as described in claim 1, characterized in that: In step S1, based on the data from the Third National Land and Resources Survey and satellite imagery, 0.5-meter high-resolution remote sensing images are used to divide the study area into independent ecological patch units through multi-scale segmentation technology. The minimum area threshold for patch division is 0.1 square kilometers, and the shape index is controlled between 0.7 and 0.9 to ensure that forest, grassland, and wetland ecosystem types are independently divided into patches.

3. The system and method for rapid classification of ecosystem restoration needs as described in claim 1, characterized in that: In step S2, a convolutional neural network model is constructed to score the ecosystem service function of patch units; the input layer data includes ecosystem type, net primary productivity, rainfall erosivity factor, and vegetation cover parameter, and the output layer generates a quantitative score of 0 to 100 for the core functions of water conservation, carbon sequestration and oxygen release. The model was trained using historical remote sensing-derived net primary productivity data and measured runoff and rainfall data from meteorological stations, and the weight parameters were optimized using a backpropagation algorithm.

4. The system and method for rapid classification of ecosystem restoration needs as described in claim 1, characterized in that: In step S3, the ten-year soil erosion modulus, vegetation coverage change trend and water quality monitoring data of Weichang Manchu and Mongolian Autonomous County are integrated to construct a dynamic damage index model; the degradation level is divided into three levels: mild degradation, moderate degradation and severe degradation.

5. The system and method for rapid classification of ecosystem restoration needs as described in claim 1, characterized in that: In step S4, a restoration priority weight matrix is ​​generated using the entropy method, integrating socio-economic factors such as population density, arable land ratio, and tourism revenue. The core water conservation area accounts for 30% of the weight, the key carbon sequestration and oxygen release area accounts for 25%, the ecotourism hotspot area accounts for 20%, and the remaining weights are dynamically allocated according to the geological disaster risk level. Data sources include agricultural water consumption, industrial water consumption, and tourist visits from the Weichang County Statistical Yearbook, ensuring that the weight calculation is accurately matched with local development needs.

6. The system and method for rapid classification of ecosystem restoration needs as described in claim 1, characterized in that: In step S5, a minimum cumulative resistance model is used in the GIS platform to generate a heat map of ecological corridor restoration. The parameter settings include a slope resistance coefficient of 0.1-0.5, a land use type resistance value of 1 for forest, 2 for grassland, and 5 for bare land. The corridor width is set as a 200-meter buffer zone based on animal migration path analysis, and the landscape connectivity index threshold is required to be no less than 0.

6. Key nodes are prioritized to connect the Saihanba Mechanical Forest Farm and the ecological barrier area of ​​the Luanhe River upstream national nature reserve.

7. The system and method for rapid classification of ecosystem restoration needs as described in claim 1, characterized in that: In step S6, a transfer learning-driven degradation pattern matching engine is developed. The training set integrates historical case data from the Saihanba Forest Farm Restoration Project and the Luanhe Wetland Restoration Project. Input features include NPP decline rate greater than 15% and soil loss exceeding 500 tons / km²·year. The engine automatically recommends the optimal engineering parameters.

8. The system and method for rapid classification of ecosystem restoration needs as described in claim 1, characterized in that: In step S7, a multi-source collaborative monitoring network is established to track the restoration effect; UAV multispectral remote sensing covers the 450-900 nm band, the ground sensor network is deployed at a density of 5 nodes per square kilometer, soil moisture is monitored once per hour, and vegetation index is updated synchronously with MODIS data every 16 days; the calibration mechanism requires the data deviation threshold to be controlled within ±5%, and abnormal data automatically triggers the manual verification process.

9. The system and method for rapid classification of ecosystem restoration needs as described in claim 1, characterized in that: In step S8, a digital twin system for ecological restoration of the entire region is constructed, integrating the GEP assessment model and the spatiotemporal simulation engine; the core parameters include rainfall change gradient ±10%, temperature fluctuation range ±2℃, and vegetation recovery cycle of 5 to 10 years; and the system predicts and outputs three types of strategies: extreme drought scenario, economic development balance model, and natural disaster emergency plan.

10. A rapid ecosystem restoration demand classification system, characterized in that: When in use, the system operates the rapid ecosystem restoration demand classification system and method as described in any one of claims 1 to 9; The system includes: Multi-source data fusion acquisition module: integrates land and resources survey data, satellite remote sensing images, meteorological monitoring data, and socio-economic statistics; Ecosystem service function quantitative assessment module: Based on a convolutional neural network model, inputting net primary productivity, rainfall erosivity factor, and vegetation cover parameters, outputting quantitative scores for water conservation, carbon sequestration and oxygen release, and soil retention functions; Ecological degradation dynamic diagnosis module: integrates soil erosion modulus, NDVI change rate, and water pollutant concentration indicators to construct a damage index model and classify mild, moderate, and severe degradation levels; Repair Priority Decision Module: The module calculates the weights of water conservation areas, carbon sequestration core areas, and ecotourism areas using the entropy method, and generates a repair priority matrix by overlaying socio-economic factors such as population density, arable land ratio, and tourism revenue. Ecological corridor intelligent planning module: Based on the minimum cumulative resistance model and landscape connectivity analysis, it automatically generates cross-patch restoration corridors in the GIS platform; Intelligent recommendation module for restoration solutions: It integrates a transfer learning engine and a historical restoration case library. After inputting the NPP decline rate and soil loss characteristics, it automatically matches restoration project cases, recommends the optimal engineering parameters such as afforestation density and mixed planting ratio, and associates restoration strategies for similar areas. Repair effectiveness tracking and calibration module: Deploy UAV multispectral remote sensing and ground sensor network to monitor vegetation index and soil moisture index in real time; Digital twin module for whole-domain ecological restoration: Construct a digital twin system driven by the GEP assessment model to simulate changes in rainfall gradient, temperature fluctuations and vegetation recovery cycle, and output prediction solutions for multiple scenarios such as extreme drought, economic balance and disaster emergency response; Visualization and Decision Support Module: Provides a GIS map interactive interface to dynamically display ecological patch delineation, restoration heat maps, corridor planning, and effectiveness tracking data.

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