A method for processing ecological environment data for ecological restoration

Abstract

The present invention relates to the technical field of ecological environment management, and in particular to an ecological environment data processing method for ecological restoration. The method comprises the following steps: obtaining satellite remote sensing data, constructing a regional elevation model and performing ecological change parameterization to obtain an ecological elevation model; obtaining environmental sensor data, integrating the elevation model, constructing an environmental sensor network, analyzing the ecological degradation trend, and obtaining degradation trend data; using the environmental sensor network and ecological degradation trend data to evaluate the ecological pollution index, statistically distribute pollutants, and obtain pollutant distribution data; evaluating the restoration complexity according to the ecological elevation model and pollutant distribution data, dividing the area to be restored, and obtaining restoration area data; calculating the restoration buffer range, formulating a restoration management strategy based on the environmental sensor network, and obtaining an ecological restoration management strategy. The present invention can improve the management efficiency of ecological restoration.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment management, and in particular to an ecological environment data processing method for ecological restoration. Background Art

[0002] Ecological restoration, as a scientific technology aimed at restoring and improving the functions of natural ecosystems, is becoming increasingly important around the world. The destruction of the ecological environment, such as land desertification, water pollution, and biodiversity loss, has become a major challenge restricting human sustainable development. Traditional methods usually rely on field surveys and manual data collection, which is not only time-consuming and labor-intensive, but also fails to fully cover all aspects of the ecosystem. For example, the survey may miss certain environmental factors, thus affecting the accuracy of the restoration effect. Manual data analysis and expert judgment may be less efficient when facing complex ecosystems. Traditional methods rely on expert experience for data analysis and decision-making, resulting in a slow analysis process and limited by the subjective judgment of experts. In the absence of comprehensive data and real-time feedback, traditional methods often make decisions based on incomplete information, which leads to unscientific restoration strategies or failure to meet actual needs. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an ecological environment data processing method for ecological restoration to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a method for processing ecological environment data for ecological restoration includes the following steps:

[0005] Step S1: Obtain satellite remote sensing data of the monitoring area, and construct a regional elevation model based on the satellite remote sensing data of the monitoring area, so as to obtain an elevation model of the monitoring area; perform parameterized description of regional ecological changes on the elevation model of the monitoring area, so as to obtain an ecological elevation model of the monitoring area;

[0006] Step S2: Acquire environmental sensor data of the monitoring area, and perform environmental sensor data fusion on the environmental sensor data of the monitoring area according to the elevation model of the monitoring area, so as to obtain an environmental sensor network of the monitoring area; perform regional ecological degradation trend analysis based on the environmental sensor network of the monitoring area, so as to obtain regional ecological degradation trend data;

[0007] Step S3: performing a regional ecological pollution index assessment based on the monitoring regional environmental sensor network and the regional ecological degradation trend data, thereby obtaining a regional ecological pollution index set; performing regional ecological pollutant distribution statistics based on the regional ecological pollution index set, thereby obtaining regional ecological pollutant distribution data;

[0008] Step S4: performing a regional ecological restoration complexity assessment based on the monitoring area ecological elevation model and the regional ecological pollutant distribution data, thereby obtaining regional ecological restoration complexity data; dividing the monitoring area elevation model into areas to be restored based on the regional ecological restoration complexity data, thereby obtaining data on areas to be restored;

[0009] Step S5: Calculate the ecological restoration buffer range based on the regional ecological pollutant distribution data and the data of the area to be ecologically restored, so as to obtain the ecological restoration buffer range data; classify and select the ecological restoration management strategy for the data of the area to be ecologically restored and the ecological restoration buffer range data based on the monitoring area environmental sensor network, so as to obtain the regional ecological restoration management strategy.

[0010] The present invention constructs a regional elevation model through satellite remote sensing data, which can accurately obtain terrain undulation, slope and elevation changes. This information is crucial for understanding the natural state and change trend of the ecosystem. Parameterized description of the elevation model can help identify the impact of different terrains on ecological changes. For example, the ecological characteristics of mountains and lowlands are different, and this description is helpful for the formulation of ecological restoration management strategies for different terrains. The elevation model provides basic data support for subsequent ecological change analysis and restoration strategies, so that ecological restoration work can be carried out on a scientific and accurate basis. Environmental sensor data can monitor multiple environmental factors such as air quality, water quality, soil moisture, etc. in real time. By integrating these data with the elevation model, a more comprehensive and detailed environmental condition can be obtained. Through the real-time data of the environmental sensor network, the degradation trend in the ecosystem, such as pollution diffusion, soil erosion, etc., can be identified. This makes the prediction of ecological degradation more accurate and provides a scientific basis for formulating effective restoration measures. Environmental sensors can provide real-time or frequently updated data, which helps to dynamically adjust the ecological restoration management strategy and ensure that the restoration measures can respond to environmental changes in a timely manner. Through the regional ecological pollution index assessment, the degree of pollution can be quantified and the impact of different pollution sources on the ecosystem can be evaluated. This assessment helps identify the main pollution problems and priority restoration areas. Statistical distribution data of pollutants can help understand the spatial distribution characteristics of pollutants in the ecosystem. This helps to develop targeted pollution control and restoration measures. Using pollution indices and distribution data to guide restoration work can make scientific decisions based on actual data rather than relying on subjective judgment. Assessing restoration complexity can help identify the main difficulties and challenges in restoration work. This helps to allocate resources reasonably and choose the most effective restoration methods. Dividing the area to be restored based on elevation models and pollutant distribution data can ensure that restoration work is concentrated in the areas most needed and avoid unnecessary waste of resources. Through the assessment of complexity, a phased restoration management strategy can be formulated to gradually solve various problems in the ecosystem and improve restoration efficiency. By calculating the ecological restoration buffer range, appropriate protection areas can be set to reduce secondary disturbance to the ecosystem during the restoration process. Based on the data of restoration areas and buffer ranges, specific restoration strategies can be formulated for different restoration areas. This classification selection ensures the targetedness and effectiveness of restoration measures. Through detailed restoration strategies, restoration resources can be allocated more reasonably to ensure that resource inputs can produce the greatest ecological benefits. In summary, these steps have greatly improved the scientificity and efficiency of ecological restoration by comprehensively using satellite remote sensing data, environmental sensor data, and data analysis technology. From data acquisition and analysis to strategy formulation, the optimization of each link provides solid support for the restoration and protection of the ecosystem. This systematic approach can better deal with complex problems in the ecological environment and improve the success rate and sustainability of ecological restoration management.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: acquiring satellite remote sensing data of the monitoring area, and performing image registration on the satellite remote sensing data of the monitoring area, thereby obtaining satellite remote sensing registration data of the monitoring area;

[0013] Step S12: geometrically correcting the satellite remote sensing registration data of the monitoring area to obtain satellite remote sensing correction data of the monitoring area, and converting the satellite remote sensing correction data of the monitoring area into regional satellite image elevation values ​​to obtain satellite elevation data of the monitoring area;

[0014] Step S13: performing Kriging interpolation terrain modeling based on the satellite elevation data of the monitoring area, thereby obtaining an elevation model of the monitoring area;

[0015] Step S14: extracting remote sensing spectral features from the satellite remote sensing correction data of the monitoring area, thereby obtaining spectral data of the monitoring area, and performing regional ecological change analysis based on the spectral data of the monitoring area, thereby obtaining ecological time series change data of the monitoring area;

[0016] Step S15: Parameterizing the regional ecological changes of the monitoring area elevation model according to the ecological time series change data of the monitoring area, thereby obtaining the ecological elevation model of the monitoring area.

[0017] The present invention aligns multi-temporal or multi-source satellite remote sensing images through image registration so that they accurately correspond in geographic coordinates. This is a basic step when analyzing data acquired at different time points or different satellite sensors. Effective registration improves the accuracy of data fusion and helps with subsequent analysis. The obtained registration data can eliminate geometric errors between images and improve the contrast and consistency between different images. This is particularly important for long-term environmental monitoring and change detection. Through geometric correction, the geometric distortion in the satellite image is corrected so that the geographic coordinate system of the image is more consistent with the actual terrain. This improves the accuracy of the image and facilitates further analysis and application. Converting the height information in the image into unified elevation data helps to accurately represent the three-dimensional characteristics of the terrain. This provides reliable basic data for subsequent terrain analysis and modeling. Kriging interpolation is a commonly used geostatistical method for estimating the elevation value of an unknown area from a known elevation point. This method can generate a smooth elevation model, which helps to understand the terrain characteristics of the monitored area. By constructing an elevation model, important information can be provided for further terrain analysis, planning and decision-making. For example, this can help identify potential flood areas or assess the impact of terrain on construction projects. Extracting spectral features from remote sensing images can help identify different types of objects and their changes. This is crucial for classifying objects, detecting vegetation health, and assessing land use changes. By analyzing the temporal changes in spectral data, the dynamic changes of ecosystems can be monitored, such as changes in vegetation coverage and land use conversion. This provides a scientific basis for ecological protection and resource management. Combining ecological temporal change data with elevation models can create a comprehensive ecological elevation model. This model not only contains topographic information, but also combines the dynamic characteristics of ecological changes, thereby reflecting the environmental conditions of the region more comprehensively. Parameterizing ecological changes can make complex ecological change processes quantifiable and easy to analyze. This helps to understand the impact of ecological changes on elevation models and how to optimize management and protection strategies based on these changes.

[0018] Optionally, step S14 is specifically:

[0019] Step S141: extracting remote sensing spectral features from satellite remote sensing correction data of the monitoring area, thereby obtaining spectral data of the monitoring area;

[0020] Step S142: classifying the spectral data of the monitoring area by band reflectance characteristics, thereby obtaining regional blue band reflectance data and regional green band reflectance data;

[0021] Step S143: dividing the spectral data of the monitoring area into water areas according to the regional blue band reflectance data, thereby obtaining the water area data of the monitoring area; dividing the spectral data of the monitoring area into vegetation areas according to the regional green band reflectance data, thereby obtaining the vegetation data of the monitoring area;

[0022] Step S144: dividing the regional water area according to the water body data of the monitored area, thereby obtaining regional water area data, and performing time series change statistics of the regional water area on the regional water area data, thereby obtaining the monitoring area water area change data;

[0023] Step S145: Calculating the regional vegetation coverage rate according to the vegetation data of the monitoring area, thereby obtaining the regional vegetation coverage rate data, and performing time series change statistics of the regional vegetation coverage rate on the regional vegetation coverage rate data, thereby obtaining the vegetation coverage rate change data of the monitoring area;

[0024] Step S146: Time series merging the water area change data and the vegetation coverage change data of the monitoring area to obtain the ecological time series change data of the monitoring area.

[0025] The spectral feature extraction of the present invention helps to obtain the basic spectral data of the monitoring area, which provides the necessary raw data support for subsequent analysis. Band reflectance feature classification can distinguish data of different bands, so that specific blue band and green band reflectance data can be extracted from them for further analysis of water bodies and vegetation. Water body area division based on blue band reflectance and vegetation area division based on green band reflectance can accurately identify and classify water bodies and vegetation areas in the monitoring area, providing key data for environmental analysis of specific areas. Regional water area division and its time series change statistics can provide detailed water area change information, which is crucial for understanding the dynamic changes of water bodies and ecological monitoring. Calculating regional vegetation coverage and performing time series change statistics can help evaluate the changing trend of vegetation coverage and the ecological health status. The water area change data and vegetation coverage change data are time-series merged to finally provide comprehensive ecological time series change data, which helps to fully understand the overall changes and trends of the ecological environment.

[0026] Optionally, step S2 specifically includes:

[0027] Step S21: Acquire monitoring area environmental sensor data, and perform sensor space feature extraction on the monitoring area environmental sensor data, thereby obtaining monitoring area environmental sensor space data;

[0028] Step S22: integrating the spatial data of the environmental sensors in the monitoring area into a sensor spatial coordinate system based on the elevation model of the monitoring area, thereby obtaining the sensor spatial coordinate system of the monitoring area;

[0029] Step S23: fusing the monitoring area environment sensor data according to the monitoring area sensor spatial coordinate system, thereby obtaining the monitoring area environment sensor network;

[0030] Step S24: collecting geological sensor data of the monitoring area and water quality sensor data of the monitoring area based on the monitoring area environmental sensor network, thereby obtaining geological sensor data of the monitoring area and water quality sensor data of the monitoring area;

[0031] Step S25: Perform regional ecological degradation trend analysis based on the geological sensor data and the water quality sensor data of the monitoring area, so as to obtain regional ecological degradation trend data.

[0032] By acquiring environmental sensor data, the present invention can monitor and record various environmental factors (such as temperature, humidity, air pressure, etc.) in the monitoring area in real time, and these data are helpful to obtain basic information about the environment. By extracting spatial features from sensor data, the spatial distribution characteristics of the area covered by the sensor can be identified and analyzed, and the spatial patterns and change trends in the data can be identified. This helps to understand the spatial distribution of environmental data, and then formulate more targeted management measures. Through the application of elevation models, the data of the sensor can be accurately mapped to the real geographic coordinate system, which is crucial for understanding the actual geographical distribution of the data. Coordinate system integration can ensure the spatial consistency of data from different sensors, making the data fusion process more reliable, thereby reducing data processing errors caused by coordinate differences. Through data fusion, data from different sensors are combined to provide more comprehensive environmental information. For example, combining temperature, humidity and air quality data can provide a more comprehensive understanding of the environmental conditions of the monitoring area. The fused data integrates the information of multiple sensors, improving the efficiency of data use and decision support capabilities. Geological and water quality data collection based on the environmental sensor network can achieve multi-dimensional monitoring of the monitoring area and capture more information about geological characteristics and water quality conditions. By acquiring geological and water quality data, a comprehensive assessment of the geological conditions and water pollution in the region can be made, thereby supporting environmental protection and resource management decisions. By analyzing geological and water quality data, the degradation trend of the regional ecosystem, such as land degradation and worsening water pollution, can be identified. This helps to detect potential environmental problems in a timely manner. Understanding the trend of ecological degradation helps to formulate effective environmental protection measures, and the early warning system can take intervention measures in advance to prevent further deterioration of the ecological environment, thereby supporting regional sustainable development.

[0033] Optionally, step S25 is specifically:

[0034] Step S251: extracting geological element content features from the geological sensor data in the monitoring area, thereby obtaining geological element content data in the monitoring area, and performing Kriging spatial interpolation on the geological element content data in the monitoring area, thereby obtaining regional continuous spatial geological element content data;

[0035] Step S252: extracting water quality element content features from the water quality sensor data in the monitoring area, thereby obtaining water quality element content data in the monitoring area, and performing spline space interpolation on the water quality element content data in the monitoring area, thereby obtaining regional continuous space water quality element content data;

[0036] Step S253: performing a regression trend surface analysis of the geological element content data in the regional continuous space, thereby obtaining the geological element content trend data; performing a regression trend surface analysis of the water quality element content data in the regional continuous space, thereby obtaining the water quality element content trend data;

[0037] Step S254: Acquire nutrient element rules, and integrate the geological element content trend data and the water quality element content trend data with the regional ecological element degradation trend according to the nutrient element rules, so as to obtain regional ecological degradation trend data.

[0038] The present invention can obtain the continuous spatial distribution of geological elements in the region by extracting features from geological sensor data and performing Kriging spatial interpolation. This helps to identify the spatial variation patterns of geological elements and provide detailed information for geological exploration and resource management. Water quality data obtains continuous spatial data through feature extraction and spline spatial interpolation methods, which helps to evaluate the distribution of water quality elements in different regions. This is crucial for water pollution monitoring and water resource management. Regression trend surface analysis helps to understand the long-term variation trends of geological and water quality elements. This analysis can reveal potential environmental changes or pollution sources and provide a basis for scientific research and environmental protection strategies. By integrating geological and water quality trend data and based on nutrient element rules, the comprehensive trend of regional ecological degradation can be obtained. This helps to identify potential problems in the ecosystem, formulate effective restoration and protection measures, and promote the sustainable development of the ecological environment.

[0039] Optionally, step S254 is specifically:

[0040] Rules for obtaining nutrients;

[0041] Conduct principal component analysis on the geological element content trend data and water quality element content trend data to obtain the principal component content trend data of ecological elements;

[0042] According to the trend data of the main component content of ecological elements, the correlation between geological elements and water quality elements is identified, so as to obtain the correlation data of ecological element content;

[0043] According to the nutrient element rules, the nutrient element trend data of the main component content of ecological elements are integrated to obtain the nutrient element trend data;

[0044] The nutrient element impact factor is calculated based on the nutrient element trend data and the ecological element content correlation data, so as to obtain the nutrient element impact factor;

[0045] Based on the influencing factors of nutrient elements, the trend data of the main component content of ecological elements are integrated with the content trend of nutrient element influencing factors to obtain the trend data of regional ecological degradation.

[0046] By acquiring and formulating rules for nutrient elements, the present invention can ensure that all data collection, analysis and interpretation follow consistent standards. This standardization helps to improve the accuracy and reliability of data processing. Nutrient element rules can provide a scientific basis for better understanding and analysis of element trends in ecosystems. This is crucial for formulating environmental protection strategies and policies. Principal component analysis (PCA) can reduce high-dimensional data to several main components, reduce data complexity, and retain most of the information. This helps to more clearly identify and understand the main trends in the data. Through principal component analysis, the factors that have the greatest impact on geological and water quality elements can be identified, thereby helping to understand the main trends of these elements and their potential impact on the ecosystem. By identifying the correlation between geological elements and water quality elements, the interactive relationship between them can be revealed. For example, some geological elements may have a significant impact on the content of water quality elements, which helps to understand the material cycle and interaction in the ecosystem. This step can integrate geological and water quality data, provide more comprehensive ecological element content correlation data, and help to formulate more accurate environmental management strategies. By applying nutrient element rules, trend data of the main component content of ecological elements can be integrated to identify the trend of changes in nutrient elements. This helps to understand which nutrient elements change most significantly in the ecosystem. Understanding nutrient trends can guide the rational use and management of resources, such as improving agricultural fertilization strategies or water management measures. Calculating nutrient impact factors can assess the impact of different nutrients on the content of other elements in the ecosystem. This helps to understand how nutrients affect the overall health and stability of the ecosystem. Provide targeted indicators to support environmental protection and resource management decisions to ensure that measures can effectively improve or maintain the health of the ecosystem. By integrating the content trend data of nutrient impact factors, the impact of nutrients on the main components of the ecosystem can be comprehensively analyzed. This helps to gain a deeper understanding of the role and changes of nutrients in the ecosystem. It can predict future ecological degradation trends and provide a forward-looking basis for environmental management and protection measures. Through regional ecological degradation trend data, an ecological early warning mechanism can be established to identify potential environmental problems in a timely manner and take preventive and restoration measures. Provide regional ecological health data to support policy makers and environmental managers in making scientific decisions on resource allocation, environmental protection and ecological restoration.

[0047] Optionally, step S3 specifically includes:

[0048] Step S31: extracting regional spatial features of degradation trend from regional ecological degradation trend data, thereby obtaining regional spatial data of degradation trend;

[0049] Step S32: collecting degradation trend area environmental sensor data from the monitoring area environmental sensor network according to the degradation trend area spatial data, thereby obtaining degradation trend area environmental sensor data;

[0050] Step S33: obtaining ecological pollutant rules, and calculating regional pollutant content on the environmental sensor data of the degradation trend area according to the ecological pollutant rules, thereby obtaining regional pollutant content data;

[0051] Step S34: calculating the spatial pollutant concentration of the degradation trend region according to the regional pollutant content data and the degradation trend region spatial data, thereby obtaining the degradation trend region spatial pollutant concentration data;

[0052] Step S35: constructing a regional ecological pollution index evaluation system according to the spatial pollutant concentration data of the degradation trend region and the regional ecological degradation trend data, thereby obtaining a regional ecological pollution index evaluation system;

[0053] Step S36: evaluating the regional ecological pollution index of the monitoring area environmental sensor network according to the regional ecological pollution index evaluation system, thereby obtaining a regional ecological pollution index set;

[0054] Step S37: Perform regional ecological pollutant distribution statistics according to the regional ecological pollution index to obtain regional ecological pollutant distribution data.

[0055] The present invention can accurately identify hot spots and trends of ecological degradation by extracting regional spatial features, thereby providing specific spatial coordinates for subsequent monitoring and intervention measures. Spatial feature extraction helps to display the trend of ecological degradation in the form of maps or graphics, which is convenient for intuitive understanding and analysis. It can help determine the areas of focus, make the allocation of resources more scientific and efficient, and avoid unnecessary monitoring and intervention. The data collected by environmental sensors can provide real-time updates on environmental conditions, which helps to quickly discover ecological degradation problems. Environmental data collected based on spatial data can improve the accuracy and pertinence of monitoring, and can verify the situation of the degradation trend area through actual data to ensure the authenticity and reliability of the data. Using pollutant rules for calculation can ensure the standardization of pollutant content measurement and evaluation and reduce human errors. Calculating pollutant content by a regularized method can improve the accuracy and consistency of pollutant detection. It helps to convert complex environmental data into quantifiable pollutant content data for subsequent analysis. Combining pollutant content data with spatial data can more accurately calculate the pollutant concentration of the region, provide a comprehensive pollution assessment, help evaluate the environmental quality of a specific region, and provide data support for the formulation of improvement measures. The spatial distribution of pollutant concentrations and their changing trends can be analyzed to provide a basis for the formulation of long-term environmental protection strategies. The establishment of an ecological pollution index assessment system can comprehensively consider multiple environmental factors and provide a comprehensive evaluation of ecological pollution levels. Through statistical analysis, the distribution of pollutants in the region can be clearly understood, which is convenient for locating key governance areas, and can help optimize resource allocation and pollution control measures, making governance actions more targeted. At the same time, it can provide long-term monitoring data support, which helps to track changes in pollution control effects and environmental quality.

[0056] Optionally, step S4 specifically includes:

[0057] Step S41: extracting regional terrain features from the ecological elevation model of the monitoring area to obtain regional terrain data, and performing terrain slope feature statistics on the regional terrain data to obtain high-slope terrain regional data and low-slope terrain regional data;

[0058] Step S42: performing pollutant spatial distribution statistics on the regional ecological pollutant distribution data, thereby obtaining ecological pollutant-intensive area data and ecological pollutant-sparse area data;

[0059] Step S43: performing a regional intersection operation on the high-slope terrain area data and the ecological pollutant-intensive area data, thereby obtaining the high-remediation difficulty area data; performing a regional intersection operation on the low-slope terrain area data and the ecological pollutant-sparse area data, thereby obtaining the low-remediation difficulty area data;

[0060] Step S44: performing a regional ecological restoration complexity assessment on the data of the high restoration difficulty area and the low restoration difficulty area based on the monitoring area environmental sensor network, thereby obtaining regional ecological restoration complexity data;

[0061] Step S45: Divide the elevation model of the monitored area into areas to be restored according to the regional ecological restoration complexity data, thereby obtaining data on the areas to be restored.

[0062] The present invention can understand the terrain characteristics of the region in detail by extracting the ecological elevation model of the monitoring area and extracting the terrain characteristics. In this way, more accurate terrain data can be obtained to provide basic data support for subsequent ecological restoration work. Statistics of terrain slope characteristics and distinguishing high-slope and low-slope terrain areas help to identify which areas face greater ecological restoration difficulties. For example, high-slope areas may be more prone to erosion or soil loss, while low-slope areas may accumulate more water, leading to different types of ecological problems. Understanding the slope distribution helps to optimize the allocation of resources, so that limited restoration resources can be effectively invested in high-restoration difficulty areas, thereby improving the efficiency and effectiveness of ecological restoration work. Statistics on the spatial distribution of regional ecological pollutants can identify areas with dense and sparse pollutants. This helps to determine the most polluted areas and facilitates the priority solution of the most serious environmental problems. Understanding the distribution of pollutants helps to formulate targeted restoration strategies. For example, more stringent control measures are implemented in pollutant-intensive areas, while relatively loose measures can be taken in pollutant-sparse areas. By analyzing the spatial distribution of pollutants, environmental monitoring and data collection can be optimized to ensure that the focus is on areas with severe pollution and improve the pertinence and effectiveness of monitoring. By performing intersection operations on high-slope terrain areas and pollutant-dense areas, and low-slope terrain areas and pollutant-sparse areas, the restoration difficulty of different areas can be clarified. This helps to accurately identify which areas have higher ecological restoration difficulties, so as to make reasonable arrangements in resource allocation and restoration strategies. Clarifying the level of restoration difficulty can help prioritize areas with higher restoration difficulties, thereby effectively improving the ecological environment and reducing the long-term negative impact on the ecosystem. Understanding the distribution of high restoration difficulty areas can help assess potential ecological risks, take preventive measures in advance, and reduce the overall risk and cost of environmental restoration. By evaluating the ecological restoration complexity of the region through the environmental sensor network, detailed ecological restoration complexity data can be obtained. This helps to understand the difficulties and challenges of ecological restoration in different regions and formulate more scientific and systematic restoration plans. Complexity assessment data provides decision makers with detailed information, supports scientific decision-making, ensures the effectiveness and pertinence of restoration measures, and optimizes restoration effects. Understanding the restoration complexity of different regions helps to allocate resources reasonably and ensure that restoration measures can be effectively implemented when resources are limited. Dividing the area to be restored according to the ecological restoration complexity data can ensure that restoration work can be concentrated in the areas that need to be restored, avoid waste of resources, and improve restoration effects. After dividing the area to be restored, a detailed restoration implementation plan can be formulated to clarify the restoration goals, methods and steps for each area, and improve the organization and systematization of the restoration work. Dividing the area to be restored also facilitates subsequent monitoring and evaluation, tracking the restoration effect, and timely adjusting the restoration strategy to ensure continuous improvement of the ecological environment.

[0063] Optionally, step S44 is specifically:

[0064] Step S441: extracting regional temperature features and regional humidity features based on the monitoring regional environmental sensor network, thereby obtaining regional temperature data and regional humidity data, and constructing a living environment parameter space based on the regional temperature data and regional humidity data;

[0065] Step S442: acquiring biological species growth rules, and screening biologically suitable environmental regions in the living environment parameter space according to the biological species growth rules, thereby obtaining biologically suitable environmental region data;

[0066] Step S443: calculating the suitable environment area overlap of the biological suitable environment area data, thereby obtaining suitable environment area overlap data, and integrating regional biological species diversity according to the suitable environment area overlap data, thereby obtaining regional species diversity data;

[0067] Step S444: classifying regional species diversity according to the regional species diversity data, thereby obtaining complex species diversity regional data and single species diversity regional data;

[0068] Step S445: performing a regional intersection operation on the complex species diversity regional data and the high restoration difficulty regional data, thereby obtaining low ecological restoration complexity regional data; performing a regional intersection operation on the single species diversity regional data and the low restoration difficulty regional data, thereby obtaining high ecological restoration complexity regional data;

[0069] Step S446: Perform regional spatial merging of the high ecological restoration complexity area data and the low ecological restoration complexity area data to obtain regional ecological restoration complexity data.

[0070] The present invention helps to understand the basic situation of the environment by extracting regional temperature and humidity characteristics and constructing a living environment parameter space, and provides reliable basic data for subsequent biological suitable environment screening. Obtaining biological species growth rules and screening suitable environmental areas can ensure that the selected areas meet the growth requirements of specific organisms, thereby improving the pertinence and effectiveness of environmental management. Calculating the overlap of suitable environmental areas and integrating species diversity can help identify the overlap of species distribution in ecosystems, so as to better understand the biodiversity in the region. Through species diversity classification, the region can be divided into complex and single species areas, which helps to customize ecological restoration plans to meet different ecological challenges. Intersection operations on areas of different complexity can effectively distinguish between low and high ecological restoration difficulty areas, help prioritize areas with lower restoration difficulty, and optimize resource allocation. Merging high and low ecological restoration complexity area data provides a comprehensive complexity assessment for regional ecological restoration, which helps to formulate targeted restoration strategies and resource allocation plans.

[0071] Optionally, step S5 specifically includes:

[0072] Step S51: extracting pollutant characteristics from the regional ecological pollutant distribution data to obtain ecological pollutant data, and performing pollutant characteristic analysis on the ecological pollutant data to obtain ecological pollutant characteristic data;

[0073] Step S52: performing regional environmental integration based on the monitoring area environmental sensor network to obtain monitoring area environmental data, and constructing a Gaussian plume pollutant diffusion model based on the monitoring area environmental data and ecological pollutant characteristic data to obtain a pollutant diffusion model;

[0074] Step S53: Calculating the pollutant diffusion range according to the pollutant diffusion model, thereby obtaining pollutant diffusion range data;

[0075] Step S54: performing buffer zone overlay analysis based on the pollutant diffusion range data and the data of the area to be ecologically restored, thereby obtaining buffer zone overlay area data, and performing buffer range calculation based on the buffer zone overlay area data, thereby obtaining ecological restoration buffer range data;

[0076] Step S55: Classify and select ecological restoration management strategies for the ecological restoration area data and the ecological restoration buffer range data based on the monitoring area environmental sensor network, so as to obtain the regional ecological restoration management strategy.

[0077] The present invention can obtain more accurate pollutant data by extracting features from regional ecological pollutant distribution data, which helps to understand the distribution of different pollutants in the ecosystem. Pollutant characteristic analysis can reveal the chemical properties, sources and potential impacts of pollutants on the environment, and provide a scientific basis for the formulation of targeted governance measures. The ecological pollutant characteristic data obtained by analysis can provide data support for environmental management and policy formulation, ensuring the effectiveness and pertinence of measures. By integrating environmental data through the monitoring regional environmental sensor network, comprehensive regional environmental information, including meteorology, topography, etc., can be obtained, which plays an important role in accurately assessing the spread of pollutants. Constructing a Gaussian plume pollutant diffusion model based on environmental data and pollutant characteristic data helps to predict the diffusion of pollutants in the atmosphere, and then assess the impact on different regions. The model can help predict the concentration distribution and impact range of pollutants, thereby identifying potential environmental risk areas and providing a scientific basis for preventing and responding to pollution. Calculating the diffusion range of pollutants can accurately determine the impact area of ​​pollutants on the environment, which helps to formulate more effective intervention measures. Diffusion range data provides an important reference for environmental protection and governance decisions, such as determining the priority and scope of pollution source control. By understanding the diffusion range of pollutants, resources can be allocated more effectively to ensure the efficiency and economy of governance actions. Through buffer overlay analysis, the areas most affected by pollution can be identified, providing clear target areas for ecological restoration. Buffer range calculation helps to determine the priority and scope of restoration actions, thereby optimizing resource allocation and improving the effectiveness of ecological restoration. Comprehensive consideration of pollutant diffusion range and ecological restoration area data can achieve more comprehensive environmental management and restoration planning. Based on environmental sensor networks and restoration range data, more accurate and effective ecological restoration management strategies can be formulated, and corresponding measures can be taken for different types of pollution and restoration needs. Effective management strategies can improve the efficiency of ecological restoration, reduce resource waste, and ensure the scientific nature and operability of restoration work. Through precise classification and strategy selection, long-term protection and improvement of the ecological environment can be achieved, and the ecological quality and environmental health of the region can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0079] Figure 1 A schematic diagram of the steps of the ecological environment data processing method for ecological restoration of the present invention;

[0080] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0081] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0082] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0083] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0084] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0085] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0086] To achieve this, please refer to Figures 1 to 3 The present invention provides an ecological environment data processing method for ecological restoration, the method comprising the following steps:

[0087] Step S1: Obtain satellite remote sensing data of the monitoring area, and construct a regional elevation model based on the satellite remote sensing data of the monitoring area, so as to obtain an elevation model of the monitoring area; perform parameterized description of regional ecological changes on the elevation model of the monitoring area, so as to obtain an ecological elevation model of the monitoring area;

[0088] In this embodiment, high-resolution image data (such as image data from Landsat8 or Sentinel-2 satellites) of the monitoring area are obtained through satellite remote sensing technology. Using these image data, the digital elevation model (DEM) tool in the geographic information system (GIS) software is used to construct an elevation model of the monitoring area. The elevation model includes information such as terrain undulations, slopes, and altitudes in the area. Next, the vegetation coverage, soil type, and hydrological characteristics of the area are parameterized by the ecological model to generate a regional ecological elevation model, which describes in detail the ecological environment characteristics under different geomorphic conditions, such as vegetation type distribution and ecosystem service functions.

[0089] Step S2: Acquire environmental sensor data of the monitoring area, and perform environmental sensor data fusion on the environmental sensor data of the monitoring area according to the elevation model of the monitoring area, so as to obtain an environmental sensor network of the monitoring area; perform regional ecological degradation trend analysis based on the environmental sensor network of the monitoring area, so as to obtain regional ecological degradation trend data;

[0090] In this embodiment, environmental sensor data from the monitoring area is collected, including information such as air quality (PM2.5, CO2 concentration), soil moisture, temperature and precipitation. These data can be obtained in real time through a sensor network deployed in the monitoring area. Then, the environmental sensor data is spatially registered and fused using the elevation model to ensure the accuracy and consistency of the data. For example, the sensor data is mapped to different areas on the elevation model to generate an environmental sensor network. Trend analysis is performed using these fused data, such as using time series analysis methods to identify patterns and trends in ecological degradation, such as vegetation decline, soil erosion, etc.

[0091] Step S3: performing a regional ecological pollution index assessment based on the monitoring regional environmental sensor network and the regional ecological degradation trend data, thereby obtaining a regional ecological pollution index set; performing regional ecological pollutant distribution statistics based on the regional ecological pollution index set, thereby obtaining regional ecological pollutant distribution data;

[0092] In this embodiment, the ecological pollution index of the region is calculated using data obtained from the environmental sensor network (for example, by weighted calculation of the air quality index, soil pollution index, etc.). These indices can comprehensively reflect the pollution level in the region. Subsequently, regional ecological pollutant distribution statistics are performed based on these pollution indices, for example, a spatial distribution map of pollutant concentrations is generated by a spatial statistical method (such as Kriging interpolation). These data can be used to identify the distribution of pollution sources and help formulate targeted pollution control strategies.

[0093] Step S4: performing a regional ecological restoration complexity assessment based on the monitoring area ecological elevation model and the regional ecological pollutant distribution data, thereby obtaining regional ecological restoration complexity data; dividing the monitoring area elevation model into areas to be restored based on the regional ecological restoration complexity data, thereby obtaining data on areas to be restored;

[0094] In this embodiment, the regional ecological pollutant distribution data and ecological elevation model are used to evaluate the complexity of ecological restoration in the region. For example, a complexity index such as a restoration difficulty scoring system is applied, which combines factors such as the degree of pollution, terrain complexity, and difficulty of vegetation restoration. Through these indicators, the areas that need to be restored as a priority are determined and marked as areas to be restored. GIS tools can be used to generate vector data of the areas to be restored, and priorities can be assigned based on different restoration difficulties, such as heavily polluted and high-slope areas being classified as high-priority restoration areas.

[0095] Step S5: Calculate the ecological restoration buffer range based on the regional ecological pollutant distribution data and the data of the area to be ecologically restored, so as to obtain the ecological restoration buffer range data; classify and select the ecological restoration management strategy for the data of the area to be ecologically restored and the ecological restoration buffer range data based on the monitoring area environmental sensor network, so as to obtain the regional ecological restoration management strategy.

[0096] In this embodiment, the ecological restoration buffer range is calculated based on the pollutant distribution data and the data of the area to be restored. The buffer range can be determined based on the pollution diffusion model (such as the Gaussian model), and an appropriate buffer zone can be set around the area to be restored to reduce the impact of pollution on the restoration area. Then, based on the data of the environmental sensor network and the buffer range data, a targeted ecological restoration management strategy is formulated, such as selecting suitable vegetation restoration technology, soil improvement measures or soil and water conservation methods. Simulation and optimization tools (such as genetic algorithms or simulated annealing algorithms) can be used to select the most effective management strategy to maximize the restoration effect and cost-effectiveness.

[0097] Optionally, step S1 specifically includes:

[0098] Step S11: acquiring satellite remote sensing data of the monitoring area, and performing image registration on the satellite remote sensing data of the monitoring area, thereby obtaining satellite remote sensing registration data of the monitoring area;

[0099] In this embodiment, satellite remote sensing data such as Landsat 8 or Sentinel-2 images of the monitoring area are obtained, and the images are registered using an image registration algorithm (such as a registration technology based on feature matching) to ensure that data collected at different times or sensors are aligned in space. For example, a SIFT (Scale Invariant Feature Transform) algorithm is used to extract key points for registration, and finally the registered image data is generated for subsequent analysis.

[0100] Step S12: geometrically correcting the satellite remote sensing registration data of the monitoring area to obtain satellite remote sensing correction data of the monitoring area, and converting the satellite remote sensing correction data of the monitoring area into regional satellite image elevation values ​​to obtain satellite elevation data of the monitoring area;

[0101] In this embodiment, the registration data is geometrically corrected using ground control points (GCP) and internal and external orientation elements to correct the geometric distortion of the image. Subsequently, the corrected data is converted to elevation values ​​using a digital elevation model (DEM), and the elevation values ​​of the satellite image are converted to actual ground elevation data using methods such as cubic spline interpolation.

[0102] Step S13: performing Kriging interpolation terrain modeling based on the satellite elevation data of the monitoring area, thereby obtaining an elevation model of the monitoring area;

[0103] In this embodiment, the Kriging interpolation method is used to perform terrain modeling on the satellite elevation data of the monitoring area. First, the elevation data points are input into the Kriging interpolation algorithm, the spatial autocorrelation is calculated and an elevation model is generated. The model can describe the spatial distribution of elevations in the area, for example, a 3D elevation model is generated to display detailed information of the regional terrain.

[0104] Step S14: extracting remote sensing spectral features from the satellite remote sensing correction data of the monitoring area, thereby obtaining spectral data of the monitoring area, and performing regional ecological change analysis based on the spectral data of the monitoring area, thereby obtaining ecological time series change data of the monitoring area;

[0105] In this embodiment, spectral feature extraction is performed on satellite remote sensing correction data, and main spectral features are extracted using techniques such as principal component analysis (PCA). Based on these spectral data, ecological change analysis is performed, such as NDVI change analysis, to generate time series change data to show the dynamic changes of the ecosystem in the monitored area.

[0106] Step S15: Parameterizing the regional ecological changes of the monitoring area elevation model according to the ecological time series change data of the monitoring area, thereby obtaining the ecological elevation model of the monitoring area.

[0107] In this embodiment, the obtained ecological time series change data is combined with the elevation model to perform a parameterized description of ecological changes on the elevation model. The parameters such as the rate of change and range can be superimposed on the corresponding spatial position in the elevation model by means of raster overlay or vector overlay technology to generate an ecological elevation model for evaluating the impact of ecological changes on the terrain.

[0108] Optionally, step S14 is specifically:

[0109] Step S141: extracting remote sensing spectral features from satellite remote sensing correction data of the monitoring area, thereby obtaining spectral data of the monitoring area;

[0110] In this embodiment, raw spectral data is extracted from satellite remote sensing images. Assume that the monitoring area is an urban lake and its surrounding green space, and the satellite data source is Landsat 8 image. Remote sensing software (such as ENVI or ERDASIMAGINE) is used for spectral correction to convert the data from raw digital values ​​(DN) to surface reflectance. The atmospheric effect, solar altitude angle and sensor characteristics are taken into account during the correction process to generate corrected spectral data. Next, the spectral characteristics of the blue band (450-510 nm) and the green band (510-550 nm) are extracted and saved as band reflectance data for subsequent classification and analysis.

[0111] Step S142: classifying the spectral data of the monitoring area by band reflectance characteristics, thereby obtaining regional blue band reflectance data and regional green band reflectance data;

[0112] In this embodiment, the extracted blue band (450-510 nm) and green band (510-550 nm) reflectance data are feature classified. The image is divided into categories such as water bodies, vegetation, and bare soil using a classification algorithm in remote sensing image processing software (e.g., support vector machine (SVM) or maximum likelihood classification (MLC)). First, training sample areas (e.g., lakes and forests) are used to extract statistical features (such as mean and standard deviation) of the blue band and green band reflectances. Then, a classification map of the blue band reflectance data and the green band reflectance data is generated by a classification algorithm to further extract the features of the regional water bodies and vegetation areas.

[0113] Step S143: dividing the spectral data of the monitoring area into water areas according to the regional blue band reflectance data, thereby obtaining the water area data of the monitoring area; dividing the spectral data of the monitoring area into vegetation areas according to the regional green band reflectance data, thereby obtaining the vegetation data of the monitoring area;

[0114] In this embodiment, the monitoring area is divided into water areas based on the blue band reflectance data. The threshold segmentation method is used to determine the water area according to the specific range of the blue band reflectance (such as reflectance> 0.2), and a binary water body map is generated. Similarly, based on the green band reflectance data, the vegetation area is divided using threshold segmentation or supervised classification algorithm. The green band reflectance threshold (such as reflectance> 0.3) is set to distinguish the vegetation area and generate a binary vegetation map. These images are used to generate water body data and vegetation data of the monitoring area.

[0115] Step S144: dividing the regional water area according to the water body data of the monitored area, thereby obtaining regional water area data, and performing time series change statistics of the regional water area on the regional water area data, thereby obtaining the monitoring area water area change data;

[0116] In this embodiment, the water body data of the monitored area is divided into regional water areas, and the time series change of the water area is calculated. The water body area data is superimposed according to the time series, and the water area at each time point is measured using the spatial analysis tool in the GIS software (such as ArcGIS). The water area time series data is generated, and the change trend and annual average change rate of the water area are calculated using statistical analysis software (such as R or Python's Pandas library). The data on the change of the water area in the monitored area is obtained, including the magnitude and rate of change of the area increase or decrease.

[0117] Step S145: Calculating the regional vegetation coverage rate according to the vegetation data of the monitoring area, thereby obtaining the regional vegetation coverage rate data, and performing time series change statistics of the regional vegetation coverage rate on the regional vegetation coverage rate data, thereby obtaining the vegetation coverage rate change data of the monitoring area;

[0118] In this embodiment, the regional vegetation coverage is calculated based on the vegetation data, and the time series change statistics are performed. First, the vegetation coverage area at each time point is extracted from the vegetation area data, and the vegetation coverage is calculated by counting the proportion of the number of pixels in the vegetation area to the total number of pixels in the area. Use a time series analysis tool (such as Python's Matplotlib or Seaborn library) to draw a time series graph of the vegetation coverage and analyze its changing trend. Finally, the change data of the vegetation coverage in the monitoring area is generated, including the fluctuation and trend of the coverage.

[0119] Step S146: Time series merging the water area change data and the vegetation coverage change data of the monitoring area to obtain the ecological time series change data of the monitoring area.

[0120] In this embodiment, the water area change data and the vegetation coverage change data are merged in time series. First, the two types of time series data are integrated into one data set to ensure the alignment of timestamps. Use data analysis tools (such as Excel or Python's data processing library) to merge the water area change data and the vegetation coverage change data to create a comprehensive ecological time series change data set. By drawing a time series diagram and calculating the correlation coefficient, the relationship between the change in water area and the change in vegetation coverage is analyzed to obtain the overall ecological time series change data of the monitoring area. This helps to evaluate the changing trends of the regional ecological environment and their mutual influence.

[0121] Optionally, step S2 specifically includes:

[0122] Step S21: Acquire monitoring area environmental sensor data, and perform sensor space feature extraction on the monitoring area environmental sensor data, thereby obtaining monitoring area environmental sensor space data;

[0123] In this embodiment, when obtaining environmental sensor data in the monitoring area, it is first necessary to install multiple environmental sensors (such as temperature and humidity sensors, air quality sensors) at different locations in the monitoring area. These sensors should be able to collect data such as meteorology and pollutant concentration in real time. Subsequently, the sensor spatial features are extracted, and the data is processed using spatial interpolation technology (such as Kriging interpolation) to generate environmental sensor spatial data covering the entire monitoring area. This step requires spatial statistical analysis of the collected data and fusion of data from different sensors to create a detailed environmental sensor spatial feature map.

[0124] Step S22: integrating the spatial data of the environmental sensors in the monitoring area into a sensor spatial coordinate system based on the elevation model of the monitoring area, thereby obtaining the sensor spatial coordinate system of the monitoring area;

[0125] In this embodiment, the sensor spatial data is integrated based on the elevation model of the monitoring area. The elevation model (such as the digital elevation model DEM) provides the terrain information of the area, which is used to adjust the coordinate system of the sensor data. For example, the coordinates of the sensor data are converted from the original ground coordinate system to the coordinate system after the elevation correction. This step ensures that the spatial position of the data is consistent with the actual terrain, and the spatial data of the environmental sensors in the monitoring area are integrated using a coordinate conversion algorithm (such as a coordinate transformation matrix) to obtain an accurate sensor spatial coordinate system.

[0126] Step S23: fusing the monitoring area environment sensor data according to the monitoring area sensor spatial coordinate system, thereby obtaining the monitoring area environment sensor network;

[0127] In this embodiment, the environmental sensor data is fused according to the integrated sensor spatial coordinate system. The data from different sensors are synthesized using a data fusion algorithm (such as weighted average, Kalman filter). This step includes registering the data of each sensor to ensure that the data is correctly superimposed in space and removing redundant or conflicting data. The fused data provides a complete environmental sensor network diagram of the monitoring area, including multiple environmental indicators such as meteorology and air quality.

[0128] Step S24: collecting geological sensor data of the monitoring area and water quality sensor data of the monitoring area based on the monitoring area environmental sensor network, thereby obtaining geological sensor data of the monitoring area and water quality sensor data of the monitoring area;

[0129] In this embodiment, geological and water quality data are collected based on the environmental sensor network in the monitoring area. The geological sensors may include soil moisture sensors, seismic sensors, etc., and the water quality sensors include pH sensors, turbidity sensors, etc. These sensors are arranged in the monitoring area and relevant data are collected regularly.

[0130] Step S25: Perform regional ecological degradation trend analysis based on the geological sensor data and the water quality sensor data of the monitoring area, so as to obtain regional ecological degradation trend data.

[0131] In this embodiment, geological sensor data and water quality sensor data are used to analyze the trend of regional ecological degradation. Data analysis methods (such as time series analysis and trend analysis) are applied to evaluate changes in environmental quality. For example, by analyzing historical data on soil moisture and water quality, degradation trends can be identified. Combined with statistical analysis and machine learning models, regional ecological degradation trend data is generated to provide detailed trend predictions and ecological impact assessments.

[0132] Optionally, step S25 is specifically:

[0133] Step S251: extracting geological element content features from the geological sensor data in the monitoring area, thereby obtaining geological element content data in the monitoring area, and performing Kriging spatial interpolation on the geological element content data in the monitoring area, thereby obtaining regional continuous spatial geological element content data;

[0134] In this embodiment, geological sensor data including raw data such as mineral content and element distribution are obtained from the monitoring area. These data are subjected to feature extraction algorithms, such as principal component analysis (PCA), to extract the main geological element content characteristics. Then, the feature-extracted data are spatially interpolated using the Kriging interpolation method to generate continuous spatial data of geological element content in the region. This process can be performed using geostatistical software such as GS+, in which the Kriging interpolation model is set as a global model, and the model is verified and adjusted using actual sampling data to ensure the accuracy of the interpolation results.

[0135] Step S252: extracting water quality element content features from the water quality sensor data in the monitoring area, thereby obtaining water quality element content data in the monitoring area, and performing spline space interpolation on the water quality element content data in the monitoring area, thereby obtaining regional continuous space water quality element content data;

[0136] In this embodiment, water quality sensor data including the main chemical elements and pollutant concentrations in the water are obtained from the monitoring area. These data are processed by a feature extraction algorithm (e.g., feature extraction based on Fourier transform) to extract the concentration characteristics of the main water quality elements. Then, the extracted data is spatially interpolated using a spline interpolation method (such as B-spline or cubic spline interpolation) to generate continuous spatial data of the water quality element content. The spline interpolation tool in software such as ArcGIS can be used to select the spline basis function and adjust the smoothing parameters to ensure the smoothness and accuracy of the data.

[0137] Step S253: performing a regression trend surface analysis of the geological element content data in the regional continuous space, thereby obtaining the geological element content trend data; performing a regression trend surface analysis of the water quality element content data in the regional continuous space, thereby obtaining the water quality element content trend data;

[0138] In this embodiment, the regional continuous space geological element content data is subjected to regression trend surface analysis, and a polynomial regression model (such as a quadratic or cubic polynomial) is used to fit the spatial distribution trend of the data to obtain the trend data of the geological element content. Similarly, the same regression trend surface analysis is performed on the regional continuous space water quality element content data, and a suitable regression model is selected to capture the spatial variation trend of the water quality elements. These analyses can be completed by the regression toolbox in Matlab, and the model selection is optimized by comparing the goodness of fit index (such as the R square value).

[0139] Step S254: Acquire nutrient element rules, and integrate the geological element content trend data and the water quality element content trend data with the regional ecological element degradation trend according to the nutrient element rules, so as to obtain regional ecological degradation trend data.

[0140] In this embodiment, the geological element content trend data and the water quality element content trend data are comprehensively analyzed according to the preset nutrient element rules, such as "ideal geological and water quality element ratio" or "element distribution standard for healthy ecosystems". By comparing these trend data with the nutrient element rules, the ecological degradation trend in the region is calculated. This can be achieved through a custom algorithm, combining the trend data and rules, using weighted average or difference analysis to integrate and obtain the final regional ecological degradation trend data. For example, if the trend of geological elements or water quality elements in certain areas deviates from the nutrient element rules, they are marked as potential ecological degradation areas.

[0141] Optionally, step S254 is specifically:

[0142] Rules for obtaining nutrients;

[0143] In the present embodiment, the acquisition of nutrient element rules can be completed by analyzing research documents, nutrition handbooks and environmental science data in related fields. For example, reference can be made to the standardized rules and formulas in "Environmental Nutrition" or "Soil Nutrition", which provide standard content ranges and influencing factors of various nutrient elements (such as nitrogen, phosphorus, potassium, etc.) in ecosystems. By consulting relevant data tables and formulas, such as plant nutrient requirement standards (plant requirements for nitrogen, phosphorus, potassium), nutrient element rules for analysis are obtained. In specific implementation, database retrieval tools such as Google Scholar or Web of Science can be used to find relevant scientific documents, and applicable rules can be formulated in conjunction with the nutrient element requirements of specific environments or regions.

[0144] Conduct principal component analysis on the geological element content trend data and water quality element content trend data to obtain the principal component content trend data of ecological elements;

[0145] In the present embodiment, when the principal component analysis is performed on the geological elements and water quality element content trend data, it is first necessary to collect the time series data of various geological and water quality elements. For example, the geological element data include the content of zinc, copper and lead in the soil, and the water quality element data include the concentration of nitrogen, phosphorus and fluorine in the water. After these data are standardized using the PCA module in statistical software such as R or Python, the PCA algorithm (such as sklearn.decomposition.PCA module) is applied to extract the main components. The calculation of the principal component can be completed by solving the eigenvalues ​​and eigenvectors of the covariance matrix, thereby obtaining the content trend data of each principal component. For example, the first principal component may represent the most significant change trend in the geological and water quality data, and these principal components can simplify the data dimension and retain the main change information.

[0146] According to the trend data of the main component content of ecological elements, the correlation between geological elements and water quality elements is identified, so as to obtain the correlation data of ecological element content;

[0147] In this embodiment, after obtaining the trend data of the main component content of the ecological elements, the correlation between the geological elements and the water quality elements is identified. For example, the correlation between the main component and the specific element (such as calcium in the soil and magnesium in the water) is determined by calculating the Pearson Correlation Coefficient. Statistical software (such as R's cor() function) is used to calculate the correlation coefficient between the main component score and the scores of each geological and water quality element, and these correlations are displayed through visualization tools (such as scatter plots or heat maps). For example, if a principal component is highly correlated with the phosphorus concentration in the water, this indicates that the phosphorus in the water quality may significantly affect the main component of the ecosystem.

[0148] According to the nutrient element rules, the nutrient element trend data of the main component content of ecological elements are integrated to obtain the nutrient element trend data;

[0149] In this embodiment, when applying the nutrient element rules to the trend data of the main component content of the ecological elements, it is first necessary to compare the main component content data with the nutrient element standards. For example, by calculating whether the main component content at each time point meets the standard nutrient element content range, the data is classified and integrated. A mathematical model (such as the weighted average method) can be used to compare the content trend of the main component with the nutrient element standard, and calculate the trend score for each time point. Assuming that the nitrogen content in a certain area exceeds the standard range, these data points are marked as high-risk areas and integrated into trend data.

[0150] The nutrient element impact factor is calculated based on the nutrient element trend data and the ecological element content correlation data, so as to obtain the nutrient element impact factor;

[0151] In this embodiment, based on the nutrient element trend data, the impact factor of the ecological element content correlation data is calculated. First, the calculation formula of the nutrient element impact factor is defined. For example, a linear regression model can be used to evaluate the degree of influence of the nutrient element trend on the change of the ecological principal component. Use statistical software (such as Python's statsmodels library) to perform regression analysis to obtain the impact factor of each ecological principal component element on the nutrient element. Set the independent variable in the regression model to the ecological principal component content trend data contained in the ecological element content correlation data, and the dependent variable to the nutrient element trend data, and calculate the regression coefficient as the impact factor.

[0152] Based on the influencing factors of nutrient elements, the trend data of the main component content of ecological elements are integrated with the content trend of nutrient element influencing factors to obtain the trend data of regional ecological degradation.

[0153] In this embodiment, when the ecological principal component content trend data is integrated using the nutrient element influencing factors, the regional ecological degradation trend data can be generated by a weighted synthesis method. First, the principal component content trend data is weighted according to the size of the influencing factor. For example, if the influencing factor of a certain nutrient element is high, its weight in the ecological degradation trend should be increased. The principal component data is weighted using the weighted average method to obtain a comprehensive ecological degradation trend. Time series analysis tools (such as R's forecast package) can be used for trend forecasting and to generate regional ecological degradation trend maps, so as to provide early warning and management of future ecological changes.

[0154] Optionally, step S3 specifically includes:

[0155] Step S31: extracting regional spatial features of degradation trend from regional ecological degradation trend data, thereby obtaining regional spatial data of degradation trend;

[0156] In this embodiment, different ecological factors in the region (such as vegetation coverage, soil moisture, climate conditions, etc.) are analyzed, and the spatial characteristics of ecological degradation are extracted using remote sensing images and ground survey data. GIS technology is used to convert these characteristics into spatial data, and the degradation trend area is marked. For example, satellite images are used to obtain regional vegetation change data, and the areas with reduced vegetation are identified through change detection algorithms, thereby forming a spatial data layer of the degradation trend area. The spatial characteristics of the area containing the ecological degradation trend in the regional ecological degradation trend data can also be extracted to obtain the specific location and area spatial characteristics of the area.

[0157] Step S32: collecting degradation trend area environmental sensor data from the monitoring area environmental sensor network according to the degradation trend area spatial data, thereby obtaining degradation trend area environmental sensor data;

[0158] In this embodiment, an environmental sensor network is arranged in the determined degradation trend area, including air quality monitors, soil moisture sensors, etc., to collect environmental data. The sensor should have high precision and real-time data transmission functions to ensure the accuracy and timeliness of the data. For example, a wireless sensor network (WSN) is used to deploy multi-point monitoring equipment in the degradation area to collect the concentration of pollutants in the air and the degree of soil pollution in real time. The environmental sensor data of the area in the monitoring area environmental sensor network can also be extracted according to the spatial data of the degradation trend area to obtain the environmental sensor data of the degradation trend area.

[0159] Step S33: obtaining ecological pollutant rules, and calculating regional pollutant content on the environmental sensor data of the degradation trend area according to the ecological pollutant rules, thereby obtaining regional pollutant content data;

[0160] In this embodiment, the collected environmental data is analyzed according to the rules of ecological pollutants (such as local environmental standards) to calculate the content of pollutants. The rules may include standard limits for different pollutants or calculation formulas for pollutant concentrations. For example, according to air quality standards, the PM2.5 and PM10 concentration data collected by the sensor are converted into pollutant content data that meets environmental standards.

[0161] Step S34: calculating the spatial pollutant concentration of the degradation trend region according to the regional pollutant content data and the degradation trend region spatial data, thereby obtaining the degradation trend region spatial pollutant concentration data;

[0162] In this embodiment, the regional pollutant content data and the degradation trend regional spatial data are used to calculate the pollutant concentration distribution in the region through spatial analysis methods (such as spatial interpolation, Kriging interpolation, etc.). For example, the pollutant content data of different locations are interpolated through a geographic information system (GIS) to generate a spatial distribution map of regional pollutant concentration.

[0163] Step S35: constructing a regional ecological pollution index evaluation system according to the spatial pollutant concentration data of the degradation trend region and the regional ecological degradation trend data, thereby obtaining a regional ecological pollution index evaluation system;

[0164] In this embodiment, the pollutant concentration distribution in the region is calculated by spatial analysis methods (such as spatial interpolation, Kriging interpolation, etc.) using regional pollutant content data and degradation trend regional spatial data. The pollutant content data of different locations can be interpolated by a geographic information system (GIS) to generate a spatial distribution map of regional pollutant concentration. For example, the calculation formula and evaluation criteria of the regional ecological pollution index are defined. The ecological pollution index is a comprehensive indicator that comprehensively considers factors such as pollutant concentration, ecosystem health, and environmental change trends. Existing ecological pollution index models, such as ecological footprint models, environmental quality index (EQI), etc., can be referenced, or a new indicator system can be designed according to specific needs. The main factors affecting the ecological pollution index are selected from the regional ecological degradation trend data. For example, the influencing factors may include pollutant concentration, degree of damage to the ecosystem, soil quality, water quality indicators, etc. Each factor needs to be quantified for use in the assessment. Weights are assigned to each factor. These weights can be determined based on expert opinions, historical data analysis, or statistical methods (such as principal component analysis). The setting of weights should reflect the relative importance of the factors in the overall assessment. Each selected factor is standardized so that they have the same dimension. For example, pollutant concentrations can be converted into standardized values ​​(z-scores). All standardized factors can be combined into a comprehensive ecological pollution index using weighted average, linear regression or other statistical methods. The specific calculation formula can be as follows: ;in For the The ecological pollution index of a region, For the The weight of a factor. For the Region No. The standardized value of the factor. is the total number of factors. Based on the calculation results, a regional ecological pollution index evaluation system is established, including classifying it into different categories according to the range of the pollution index, such as light pollution, moderate pollution, and severe pollution.

[0165] Step S36: evaluating the regional ecological pollution index of the monitoring area environmental sensor network according to the regional ecological pollution index evaluation system, thereby obtaining a regional ecological pollution index set;

[0166] In this embodiment, the ecological pollution in the monitoring area is comprehensively evaluated based on the constructed regional ecological pollution index evaluation system. The evaluation system may include indicators such as pollutant concentration, ecological impact factor, trend change, etc. For example, a weighted evaluation model including pollutant concentration, ecological health impact and change trend is designed, and the overall ecological pollution index is calculated based on the score of each indicator.

[0167] Step S37: Perform regional ecological pollutant distribution statistics according to the regional ecological pollution index to obtain regional ecological pollutant distribution data.

[0168] In this embodiment, the spatial distribution of pollutants is counted and displayed by analyzing the regional ecological pollution index. A pollutant distribution map can be generated to show the degree of pollution in each region. For example, based on the ecological pollution index and regional pollutant concentration data, a map drawing tool is used to generate a heat map of the distribution of pollutants in different regions for intuitive display and analysis.

[0169] Optionally, step S4 specifically includes:

[0170] Step S41: extracting regional terrain features from the ecological elevation model of the monitoring area to obtain regional terrain data, and performing terrain slope feature statistics on the regional terrain data to obtain high-slope terrain regional data and low-slope terrain regional data;

[0171] In this embodiment, high-resolution remote sensing images and digital elevation models (DEM) are used to generate terrain data for the area. The terrain data includes the elevation value of each pixel. The DEM is subjected to slope analysis by geographic information system (GIS) software to calculate the slope value of each pixel. The slope calculation can use the following formula: slope (degrees) = arctan (slope ratio) × (180 / π). According to the calculation results, the area is divided into a high slope area (slope value greater than 15 degrees) and a low slope area (slope value less than or equal to 15 degrees). Python is used to write scripts to implement these calculations, and the data of the high slope area and the low slope area are exported to GIS-compatible raster format files.

[0172] Step S42: performing pollutant spatial distribution statistics on the regional ecological pollutant distribution data, thereby obtaining ecological pollutant-intensive area data and ecological pollutant-sparse area data;

[0173] In this embodiment, environmental monitoring data, including air quality monitoring stations, soil testing data, and water pollution data, are used to construct a pollutant distribution model. These data are imported into the GIS platform, and a spatial distribution map of pollutant concentrations is generated by a spatial interpolation method (such as Kriging interpolation). Then, these data are divided into pollutant-intensive areas (pollutant concentrations greater than a certain threshold, such as 50 µg / m³) and pollutant-sparse areas (pollutant concentrations less than or equal to 50 µg / m³). Python scripts are used for data processing and statistics, and the results are saved as two raster layers: a dense area layer and a sparse area layer. It is also possible to perform threshold division based on the regional ecological pollutant distribution data that has been obtained, and the pollutant concentration greater than a certain threshold, such as 50 µg / m³, is divided into a pollutant-intensive area.

[0174] Step S43: performing a regional intersection operation on the high-slope terrain area data and the ecological pollutant-intensive area data, thereby obtaining the high-remediation difficulty area data; performing a regional intersection operation on the low-slope terrain area data and the ecological pollutant-sparse area data, thereby obtaining the low-remediation difficulty area data;

[0175] In this embodiment, the obtained high slope area data and ecological pollutant intensive area data are spatially intersected. The overlay analysis tool in GIS is used to generate high restoration difficulty area data. Similarly, the low slope area data and ecological pollutant sparse area data are intersected to obtain low restoration difficulty area data. The specific operation includes using the "spatial analysis" tool in GIS to perform "layer overlay" and save the intersection result as a new raster data file.

[0176] Step S44: performing a regional ecological restoration complexity assessment on the data of the high restoration difficulty area and the low restoration difficulty area based on the monitoring area environmental sensor network, thereby obtaining regional ecological restoration complexity data;

[0177] In this embodiment, an environmental sensor network is used to collect real-time data of high and low restoration difficulty areas, including soil moisture, air quality, and vegetation coverage. Based on these data, an ecological restoration complexity model is established to evaluate the restoration difficulty of the area. The model can consider factors such as slope, pollution level, and vegetation recovery capacity. Multivariate regression analysis or fuzzy logic methods are used to perform complexity assessment to obtain a restoration complexity index for each area. The complexity data is visualized to form a restoration complexity layer for further analysis and decision-making.

[0178] Step S45: Divide the elevation model of the monitored area into areas to be restored according to the regional ecological restoration complexity data, thereby obtaining data on the areas to be restored.

[0179] In this embodiment, based on the obtained regional ecological restoration complexity data, the threshold segmentation method is used to divide the monitoring area into restoration areas. Set the restoration complexity threshold (the level of complexity is used as the threshold, such as high complexity is considered to be a priority restoration area; such as low complexity is considered to be a non-priority restoration area), and mark the high-complexity area as the area to be restored. Use GIS software to divide the area, superimpose the boundary of the area to be restored with the elevation model, and form the final data layer of the area to be restored. This data layer can be used to formulate specific ecological restoration plans and priority sorting.

[0180] Optionally, step S44 is specifically:

[0181] Step S441: extracting regional temperature features and regional humidity features based on the monitoring regional environmental sensor network, thereby obtaining regional temperature data and regional humidity data, and constructing a living environment parameter space based on the regional temperature data and regional humidity data;

[0182] In this embodiment, multiple high-precision environmental sensors are arranged in the monitoring area to collect temperature and humidity data. The distribution of sensors should cover different terrains and vegetation types to ensure comprehensive data. Data is recorded at hourly intervals, and a spatial distribution map of regional temperature and humidity is generated by interpolation methods (such as Kriging interpolation). GIS tools are used to integrate these data into a living environment parameter space, including the spatial distribution of temperature and humidity, and their interactive effects, which provides a basis for subsequent ecological suitability assessments.

[0183] Step S442: acquiring biological species growth rules, and screening biologically suitable environmental regions in the living environment parameter space according to the biological species growth rules, thereby obtaining biologically suitable environmental region data;

[0184] In this embodiment, the growth rules of the target biological species, such as specific temperature range and humidity requirements, are obtained by consulting relevant information or expert experience. For example, for a certain specific plant, the optimum temperature is 20-30°C and the humidity is 50-70%. These growth rules are compared with the living environment parameter space generated in step S441, and the areas that meet these rules are screened out using a spatial matching algorithm (such as grid overlay analysis). These areas that meet the requirements are the biological suitable environment area data.

[0185] Step S443: calculating the suitable environment area overlap of the biological suitable environment area data, thereby obtaining suitable environment area overlap data, and integrating regional biological species diversity according to the suitable environment area overlap data, thereby obtaining regional species diversity data;

[0186] In this embodiment, overlap analysis is performed on the obtained multiple biological suitable environment areas. The Jaccard index or overlap calculation tool (such as the spatial analysis function of GIS) is used to evaluate the overlap of environmental characteristics between these areas. The overlap data calculated will help understand whether the ecological environment in different regions is similar. Based on the overlap data, the species diversity in the region is integrated by using a diversity index calculation method (such as the Shannon-Wiener index) to obtain regional species diversity data.

[0187] Step S444: classifying regional species diversity according to the regional species diversity data, thereby obtaining complex species diversity regional data and single species diversity regional data;

[0188] In this embodiment, the obtained species diversity data is used to classify the regions. For example, species richness and diversity are divided into complex species diversity regions and single species diversity regions. Complex species diversity regions refer to regions with many species and complex ecological relationships, while single species diversity regions refer to regions with few species and simple ecological structures. This can be accomplished through cluster analysis (such as K-means clustering) to classify regions according to the number of species and diversity index.

[0189] Step S445: performing a regional intersection operation on the complex species diversity regional data and the high restoration difficulty regional data, thereby obtaining low ecological restoration complexity regional data; performing a regional intersection operation on the single species diversity regional data and the low restoration difficulty regional data, thereby obtaining high ecological restoration complexity regional data;

[0190] In this embodiment, the complex species diversity area data and the high restoration difficulty area data are intersected (for example, using GIS overlay analysis tools) to obtain the low ecological restoration complexity area data. The single species diversity area data and the low restoration difficulty area data are intersected to obtain the high ecological restoration complexity area data. These operations help determine which areas have the most complex ecological restoration tasks and which are relatively simple.

[0191] Step S446: Perform regional spatial merging of the high ecological restoration complexity area data and the low ecological restoration complexity area data to obtain regional ecological restoration complexity data.

[0192] In this example, the data of high ecological restoration complexity areas and low ecological restoration complexity areas are spatially merged, and the final regional ecological restoration complexity data is generated using the overlay analysis function of GIS. This integrates regional information of all restoration complexities, provides data support for the formulation of specific restoration strategies, and helps prioritize those areas with higher restoration complexity to improve overall restoration efficiency.

[0193] Optionally, step S5 specifically includes:

[0194] Step S51: extracting pollutant characteristics from the regional ecological pollutant distribution data to obtain ecological pollutant data, and performing pollutant characteristic analysis on the ecological pollutant data to obtain ecological pollutant characteristic data;

[0195] In this embodiment, the distribution data of regional ecological pollutants, including the concentrations of various pollutants in the air (such as NO2, SO2, PM2.5, etc.), are collected from the sensor network installed in the monitoring area. The data collection time should be a continuous week to ensure the representativeness of the data. Next, the collected data is feature extracted, and statistical analysis methods such as principal component analysis (PCA) and cluster analysis are used to extract the main characteristics of the pollutants (such as the peak concentration of pollutants, fluctuation range and spatial distribution). Characteristic analysis can include time series analysis to determine the changing trend of pollutant concentrations, and spatial distribution heat maps to show the distribution patterns of pollutants in the region. For example, GIS (Geographic Information System) software is used for spatial visualization to combine pollutant concentrations with geographic locations to analyze their impact on the ecosystem.

[0196] Step S52: performing regional environmental integration based on the monitoring area environmental sensor network to obtain monitoring area environmental data, and constructing a Gaussian plume pollutant diffusion model based on the monitoring area environmental data and ecological pollutant characteristic data to obtain a pollutant diffusion model;

[0197] In this embodiment, the data collected by the environmental sensor network include environmental factors such as temperature, humidity, wind speed, and wind direction. These data are integrated into a unified database to describe the environmental conditions of the monitoring area. Then, these environmental data and pollutant characteristic data are used to construct a Gaussian plume pollutant diffusion model. The model construction process involves combining the emission source data of the pollutants (such as industrial emission points) with meteorological data (such as wind speed and wind direction), and using the Gaussian plume model formula (such as the HYSPLIT model) to simulate the diffusion process of the pollutants. It can be simulated by computer simulation tools (such as MATLAB or the SciPy library in Python), and the concentration distribution map of the pollutants at different time and space points is output.

[0198] Step S53: Calculating the pollutant diffusion range according to the pollutant diffusion model, thereby obtaining pollutant diffusion range data;

[0199] In this embodiment, the constructed Gaussian plume diffusion model is used, the actual emission and meteorological data are input, and the model is run to calculate the diffusion range of the pollutants. The range includes the area where the concentration of the pollutant gradually decreases, which is usually represented by the concentration contour line. Specifically, it can be carried out in GIS software to convert the results of the diffusion model into an isoconcentration area map. Taking an industrial emission source as an example, the influence range of the pollutant under specific wind speed and wind direction conditions is calculated, such as generating a circular area with a radius of 5 kilometers, indicating that the concentration of the pollutant in the area exceeds a certain threshold.

[0200] Step S54: performing buffer zone overlay analysis based on the pollutant diffusion range data and the data of the area to be ecologically restored, thereby obtaining buffer zone overlay area data, and performing buffer range calculation based on the buffer zone overlay area data, thereby obtaining ecological restoration buffer range data;

[0201] In this embodiment, after obtaining the pollutant diffusion range data, the location and range of the area to be ecologically restored (such as a polluted wetland) are determined. The pollutant diffusion range data and the area to be restored data are subjected to buffer overlay analysis. This uses overlay analysis tools in GIS, such as the "Buffer" function in ArcGIS, to calculate the overlap between the pollutant impact area and the restoration area. Assuming that the diffusion range data indicates that the high concentration area of ​​the pollutant overlaps the restoration area, the buffer range calculation can generate a buffer zone representing the area that needs additional protection around the restoration area. This can help plan the priority and scope of restoration work.

[0202] Step S55: Classify and select ecological restoration management strategies for the ecological restoration area data and the ecological restoration buffer range data based on the monitoring area environmental sensor network, so as to obtain the regional ecological restoration management strategy.

[0203] In this embodiment, after obtaining the ecological restoration buffer range data, the environmental characteristics of the area to be restored and its buffer zone (such as soil pollution level, vegetation restoration needs, etc.) are analyzed. Based on these data, a suitable ecological restoration management strategy is formulated, such as selecting suitable plant species for vegetation restoration, or planning soil improvement measures. A decision support system (DSS) is used to evaluate different restoration plans, and classify and select them according to cost, effect, and environmental impact. For example, biological restoration technology (such as phytoremediation) or physical restoration technology (such as soil washing) can be selected, and a detailed restoration plan and schedule can be formulated. Finally, a comprehensive regional ecological restoration management strategy report is formed to guide the actual implementation of restoration work.

[0204] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0205] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for processing ecological environment data for ecological restoration, characterized in that: The following steps are involved: Step S1: Obtain satellite remote sensing data of the monitoring area, and construct a regional elevation model based on the satellite remote sensing data of the monitoring area, so as to obtain an elevation model of the monitoring area; perform parameterized description of regional ecological changes on the elevation model of the monitoring area, so as to obtain an ecological elevation model of the monitoring area; Step S2: Acquire environmental sensor data of the monitoring area, and perform environmental sensor data fusion on the environmental sensor data of the monitoring area according to the elevation model of the monitoring area, so as to obtain an environmental sensor network of the monitoring area; Conduct regional ecological degradation trend analysis based on the monitoring regional environmental sensor network to obtain regional ecological degradation trend data; Step S3: performing a regional ecological pollution index assessment based on the monitoring regional environmental sensor network and the regional ecological degradation trend data, thereby obtaining a regional ecological pollution index set; performing regional ecological pollutant distribution statistics based on the regional ecological pollution index set, thereby obtaining regional ecological pollutant distribution data; Step S4: perform a regional ecological restoration complexity assessment based on the monitoring area ecological elevation model and the regional ecological pollutant distribution data, thereby obtaining regional ecological restoration complexity data; divide the monitoring area elevation model into areas to be restored based on the regional ecological restoration complexity data, thereby obtaining data on areas to be restored. Step S4 is specifically as follows: Step S41: extracting regional terrain features from the ecological elevation model of the monitoring area to obtain regional terrain data, and performing terrain slope feature statistics on the regional terrain data to obtain high-slope terrain regional data and low-slope terrain regional data; Step S42: performing pollutant spatial distribution statistics on the regional ecological pollutant distribution data, thereby obtaining ecological pollutant-intensive area data and ecological pollutant-sparse area data; Step S43: performing a regional intersection operation on the high-slope terrain area data and the ecological pollutant-intensive area data, thereby obtaining high-remediation difficulty area data; Perform regional intersection operations on low-slope terrain area data and ecological pollutant sparse area data to obtain low-remediation difficulty area data; Step S44: Based on the monitoring area environmental sensor network, the regional ecological restoration complexity evaluation is performed on the high restoration difficulty area data and the low restoration difficulty area data, so as to obtain the regional ecological restoration complexity data. Step S44 is specifically as follows: Step S441: extracting regional temperature features and regional humidity features based on the monitoring regional environmental sensor network, thereby obtaining regional temperature data and regional humidity data, and constructing a living environment parameter space based on the regional temperature data and regional humidity data; Step S442: acquiring biological species growth rules, and screening biologically suitable environmental regions in the living environment parameter space according to the biological species growth rules, thereby obtaining biologically suitable environmental region data; Step S443: calculating the suitable environment area overlap of the biological suitable environment area data, thereby obtaining suitable environment area overlap data, and integrating regional biological species diversity according to the suitable environment area overlap data, thereby obtaining regional species diversity data; Step S444: classifying regional species diversity according to the regional species diversity data, thereby obtaining complex species diversity regional data and single species diversity regional data; Step S445: performing a regional intersection operation on the complex species diversity regional data and the high restoration difficulty regional data, thereby obtaining low ecological restoration complexity regional data; Perform regional intersection operations on the data of single species diversity areas and low restoration difficulty areas to obtain data of high ecological restoration complexity areas; Step S446: regional spatial merging of the high ecological restoration complexity area data and the low ecological restoration complexity area data, thereby obtaining regional ecological restoration complexity data; Step S45: dividing the monitoring area elevation model into areas to be restored according to the regional ecological restoration complexity data, thereby obtaining data of the areas to be restored; Step S5: Calculate the ecological restoration buffer range according to the regional ecological pollutant distribution data and the data of the area to be restored, so as to obtain the ecological restoration buffer range data; classify and select the ecological restoration management strategy for the area data to be restored and the ecological restoration buffer range data based on the monitoring area environmental sensor network, so as to obtain the regional ecological restoration management strategy. Step S5 is specifically as follows: Step S51: extracting pollutant characteristics from the regional ecological pollutant distribution data to obtain ecological pollutant data, and performing pollutant characteristic analysis on the ecological pollutant data to obtain ecological pollutant characteristic data; Step S52: performing regional environmental integration based on the monitoring area environmental sensor network to obtain monitoring area environmental data, and constructing a Gaussian plume pollutant diffusion model based on the monitoring area environmental data and ecological pollutant characteristic data to obtain a pollutant diffusion model; Step S53: Calculating the pollutant diffusion range according to the pollutant diffusion model, thereby obtaining pollutant diffusion range data; Step S54: performing buffer zone overlay analysis based on the pollutant diffusion range data and the data of the area to be ecologically restored, thereby obtaining buffer zone overlay area data, and performing buffer range calculation based on the buffer zone overlay area data, thereby obtaining ecological restoration buffer range data; Step S55: Classify and select ecological restoration management strategies for the ecological restoration area data and the ecological restoration buffer range data based on the monitoring area environmental sensor network, so as to obtain the regional ecological restoration management strategy.

2. The ecological environment data processing method for ecological restoration according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: acquiring satellite remote sensing data of the monitoring area, and performing image registration on the satellite remote sensing data of the monitoring area, thereby obtaining satellite remote sensing registration data of the monitoring area; Step S12: geometrically correcting the satellite remote sensing registration data of the monitoring area to obtain satellite remote sensing correction data of the monitoring area, and converting the satellite remote sensing correction data of the monitoring area into regional satellite image elevation values ​​to obtain satellite elevation data of the monitoring area; Step S13: performing Kriging interpolation terrain modeling based on the satellite elevation data of the monitoring area, thereby obtaining an elevation model of the monitoring area; Step S14: extracting remote sensing spectral features from the satellite remote sensing correction data of the monitoring area, thereby obtaining spectral data of the monitoring area, and performing regional ecological change analysis based on the spectral data of the monitoring area, thereby obtaining ecological time series change data of the monitoring area; Step S15: Parameterizing the regional ecological changes of the monitoring area elevation model according to the ecological time series change data of the monitoring area, thereby obtaining the ecological elevation model of the monitoring area.

3. The ecological environment data processing method for ecological restoration according to claim 2 is characterized in that: Step S14 is specifically as follows: Step S141: extracting remote sensing spectral features from the satellite remote sensing correction data of the monitoring area, thereby obtaining spectral data of the monitoring area; Step S142: classifying the spectral data of the monitoring area by band reflectance characteristics, thereby obtaining regional blue band reflectance data and regional green band reflectance data; Step S143: dividing the spectral data of the monitoring area into water areas according to the regional blue band reflectance data, thereby obtaining the water area data of the monitoring area; dividing the spectral data of the monitoring area into vegetation areas according to the regional green band reflectance data, thereby obtaining the vegetation data of the monitoring area; Step S144: dividing the regional water area according to the water body data of the monitored area, thereby obtaining regional water area data, and performing time series change statistics of the regional water area on the regional water area data, thereby obtaining the monitoring area water area change data; Step S145: Calculating the regional vegetation coverage rate according to the vegetation data of the monitoring area, thereby obtaining the regional vegetation coverage rate data, and performing time series change statistics of the regional vegetation coverage rate on the regional vegetation coverage rate data, thereby obtaining the vegetation coverage rate change data of the monitoring area; Step S146: Time-series merging the water area change data and the vegetation coverage change data of the monitoring area to obtain the ecological time-series change data of the monitoring area.

4. The ecological environment data processing method for ecological restoration according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: Acquire monitoring area environmental sensor data, and perform sensor space feature extraction on the monitoring area environmental sensor data, thereby obtaining monitoring area environmental sensor space data; Step S22: integrating the spatial data of the environmental sensors in the monitoring area into a sensor spatial coordinate system based on the elevation model of the monitoring area, thereby obtaining the sensor spatial coordinate system of the monitoring area; Step S23: fusing the monitoring area environment sensor data according to the monitoring area sensor spatial coordinate system, thereby obtaining the monitoring area environment sensor network; Step S24: collecting geological sensor data of the monitoring area and water quality sensor data of the monitoring area based on the monitoring area environmental sensor network, thereby obtaining geological sensor data of the monitoring area and water quality sensor data of the monitoring area; Step S25: Perform regional ecological degradation trend analysis based on the geological sensor data and the water quality sensor data of the monitoring area, so as to obtain regional ecological degradation trend data.

5. The method for processing ecological environment data for ecological restoration according to claim 4, characterized in that: Step S25 is specifically as follows: Step S251: extracting geological element content features from the geological sensor data in the monitoring area, thereby obtaining geological element content data in the monitoring area, and performing Kriging spatial interpolation on the geological element content data in the monitoring area, thereby obtaining regional continuous spatial geological element content data; Step S252: extracting water quality element content features from the water quality sensor data in the monitoring area, thereby obtaining water quality element content data in the monitoring area, and performing spline space interpolation on the water quality element content data in the monitoring area, thereby obtaining regional continuous space water quality element content data; Step S253: performing a regression trend surface analysis of the geological element content data of the regional continuous space, thereby obtaining geological element content trend data; Conduct regression trend surface analysis on the water quality element content data of regional continuous space to obtain water quality element content trend data; Step S254: Acquire nutrient element rules, and integrate the geological element content trend data and the water quality element content trend data with the regional ecological element degradation trend according to the nutrient element rules, so as to obtain regional ecological degradation trend data.

6. The method for processing ecological environment data for ecological restoration according to claim 5, characterized in that: Step S254 is specifically as follows: Rules for obtaining nutrients; Conduct principal component analysis on the geological element content trend data and water quality element content trend data to obtain the principal component content trend data of ecological elements; According to the trend data of the main component content of ecological elements, the correlation between geological elements and water quality elements is identified, so as to obtain the correlation data of ecological element content; According to the nutrient element rules, the nutrient element trend data of the main component content of ecological elements are integrated to obtain the nutrient element trend data; The nutrient element impact factor is calculated based on the nutrient element trend data and the ecological element content correlation data, so as to obtain the nutrient element impact factor; Based on the influencing factors of nutrient elements, the trend data of the main component content of ecological elements are integrated with the content trend of nutrient element influencing factors to obtain the trend data of regional ecological degradation.

7. The method for processing ecological environment data for ecological restoration according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: extracting regional spatial features of degradation trend from regional ecological degradation trend data, thereby obtaining regional spatial data of degradation trend; Step S32: collecting degradation trend area environmental sensor data from the monitoring area environmental sensor network according to the degradation trend area spatial data, thereby obtaining degradation trend area environmental sensor data; Step S33: obtaining ecological pollutant rules, and calculating regional pollutant content on the environmental sensor data of the degradation trend area according to the ecological pollutant rules, thereby obtaining regional pollutant content data; Step S34: calculating the spatial pollutant concentration of the degradation trend region according to the regional pollutant content data and the degradation trend region spatial data, thereby obtaining the degradation trend region spatial pollutant concentration data; Step S35: constructing a regional ecological pollution index evaluation system according to the spatial pollutant concentration data of the degradation trend region and the regional ecological degradation trend data, thereby obtaining a regional ecological pollution index evaluation system; Step S36: evaluating the regional ecological pollution index of the monitoring area environmental sensor network according to the regional ecological pollution index evaluation system, thereby obtaining a regional ecological pollution index set; Step S37: Perform regional ecological pollutant distribution statistics according to the regional ecological pollution index to obtain regional ecological pollutant distribution data.

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