Highway network ecosystem evaluation method and system

Abstract

The invention relates to a road network ecosystem evaluation method and system, and belongs to the field of road networks, and the method comprises the steps: obtaining road network data and ecological environment data, and carrying out the data preprocessing and data integration; constructing an ecological influence evaluation index system; extracting spatial information of road network data and ecological environment data from an ecological environment influence evaluation database, and performing quantitative calculation in combination with the ecological influence evaluation index system; calculating the weight of each ecological influence evaluation index by adopting an expert scoring method to obtain a comprehensive evaluation index of the highway network ecological system; and according to the highway network ecosystem comprehensive evaluation index, dividing ecological influence grades on the highway network, generating an ecological influence evaluation report, and providing ecological optimization suggestions. According to the method and the system, multi-dimensional ecological environment data can be comprehensively considered, a scientific evaluation system and an expert scoring method are adopted, and the influence of the highway network on the ecological environment is objectively and accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of highway networks, and in particular to a highway network ecosystem evaluation method and system. Background Art

[0002] With the rapid development of global transportation infrastructure, highway network construction has become a crucial component in driving socioeconomic development. However, the impact of highway network construction and operation on the ecological environment has increasingly attracted widespread attention. Highway networks not only directly alter land use patterns but also profoundly influence climate conditions, water resource distribution, habitat connectivity, and ecosystem services. Especially in the context of global climate change, the ecological and environmental impacts of highway networks are becoming more complex and diverse. Therefore, assessing and quantifying the impact of highway networks on the ecological environment is a major issue that needs to be addressed in the field of ecological protection.

[0003] Traditional methods for assessing the ecological and environmental impacts of highway networks typically rely on single indicators, such as landscape fragmentation and ecological corridor connectivity. However, these methods often overlook the interactions between multiple influencing factors and fail to fully consider the weight and impact of different ecological and environmental factors within the ecosystem. Furthermore, existing assessment methods often focus on specific regions or ecological types, lacking a global and systematic perspective. Therefore, how to comprehensively consider and quantify multiple ecological impact factors has become a key issue in current highway network ecosystem assessment research.

[0004] Quantitative assessments of the ecological and environmental impacts of highway networks require comprehensive consideration of multiple factors, including landscape fragmentation, habitat suitability, and ecological corridor connectivity. This allows for a more accurate assessment of the extent of ecological damage and its long-term impacts caused by highway construction. Currently, with the advancement of remote sensing technology, geographic information systems (GIS), and ecological simulation models, highway network ecological impact assessments are gradually moving towards a quantitative and integrated approach. Remote sensing technology can provide data support for large-scale ecological monitoring, while GIS technology offers powerful tools for spatial analysis of ecological and environmental data. The application of ecological models, particularly species distribution models (SDMs) and landscape connectivity models, provides a scientific computational framework for ecological impact assessments. However, existing research still faces significant challenges in data integration and model application, particularly in effectively integrating data from diverse sources and formats to conduct comprehensive, multidimensional assessments.

[0005] The construction and operation of highway networks can have widespread impacts on the surrounding ecological environment, including land use change, biodiversity loss, and reduced climate regulation. Most existing assessment methods focus on quantifying or visually evaluating a single influencing factor, ignoring the complex interactions and multi-dimensional impacts within ecosystems.

[0006] The invention patent application with publication number CN117669895A discloses a highway engineering environmental impact assessment system, which includes an ecological relationship analysis module, an ecological risk prediction module, a carbon footprint assessment module, a strategic decision support module, a system impact simulation module, a data fusion processing module, an environmental strategy planning module, and a real-time monitoring feedback module. This application uses the ecological relationship analysis module to improve the recognition of ecosystem components and interactions, the use of system dynamics models and statistical analysis methods in the ecological risk prediction module to enhance the simulation capabilities of potential ecosystem impacts, and the application of time series analysis and carbon emission models in the carbon footprint assessment module to improve the analysis capabilities of highway engineering carbon footprint trends. This application can only realize ecological data analysis of highway projects, and cannot realize analysis and prediction of the entire highway network and the surrounding ecological environment, which has great limitations.

[0007] Patent application publication number CN115455534B discloses a method and system for evaluating the adaptability of highway network layout structures. The system includes: constructing a traffic flow database and a highway network structure model; analyzing and tracing highway traffic flow origins; calculating and analyzing highway network accessibility, the degree of matching between network structure centers and travel demand centers based on the traffic flow database and highway network structure model; and measuring the adaptability of the network structure layout using matching indicators. Finally, based on the adaptability calculation results, the system proposes ideas and strategies for optimizing the highway network structure. This application focuses on predicting the highway network structure layout and does not involve predictive analysis of the highway network's ecological environment. Summary of the Invention

[0008] The object of the present invention is to solve at least one of the technical drawbacks.

[0009] To this end, the purpose of the present invention is to propose a road network ecosystem evaluation method and system, which can comprehensively consider multi-dimensional ecological environment data, adopt a scientific evaluation system and expert scoring method, and objectively and accurately evaluate the impact of the road network on the ecological environment.

[0010] To achieve the above-mentioned object, an embodiment of one aspect of the present invention provides a road network ecosystem evaluation method, comprising the following steps:

[0011] S1, obtaining road network data and ecological environment data, and performing data preprocessing and data integration on the road network data and ecological environment data to obtain integrated road network data and ecological environment data, and integrating all collected data to form an ecological environment impact assessment database;

[0012] S2. Constructing an ecological impact assessment indicator system, which includes: landscape fragmentation index, habitat suitability index, ecological corridor connectivity index, ecosystem service value index, carbon sequestration capacity assessment index, and biodiversity impact index;

[0013] S3, extracting spatial information of highway network data and ecological environment data from the ecological environment impact assessment database, and combining it with the ecological impact assessment index system to perform quantitative calculations, using a deep learning model to automatically analyze and predict the data, obtaining the impact value of each indicator on the ecological environment, comparing the calculated results of each indicator with the constructed ecological impact assessment index system, substituting the extracted spatial information into the calculation of each indicator, and obtaining the corresponding quantitative results of each indicator, thereby achieving quantitative calculation of the impact on the ecological environment;

[0014] S4, using the expert scoring method to calculate the weight of each ecological impact assessment indicator, and according to the weight of each indicator, perform a weighted summation with the actual calculated indicator value to obtain the comprehensive evaluation index of the highway network ecosystem;

[0015] S5. Classify the ecological impact level of the highway network according to the comprehensive evaluation index of the highway network ecosystem, generate an ecological impact evaluation report, and provide ecological optimization suggestions based on the ecological impact level.

[0016] Furthermore, the road network data includes: spatial distribution of roads, road network density, road section characteristics and traffic flow data; the ecological environment data includes: climate data, land use data, water resources data, biodiversity data and pollution data.

[0017] Furthermore, in S1, data preprocessing and data integration are performed on the road network data and ecological environment data, including:

[0018] First, the collected road network data and ecological environment data are cleaned to remove invalid or duplicate data;

[0019] Then perform data spatial alignment and matching and convert the data format into a unified GeoJSON format standard;

[0020] Finally, all collected data are integrated to form an ecological and environmental impact assessment database.

[0021] Furthermore, the landscape fragmentation index is used to measure the degree of damage caused by the road network to the original ecological landscape; the habitat suitability index is used to evaluate the impact of the road network on the living environment of surrounding animals and plants; the ecological channel connectivity index is used to calculate the integrity of ecological corridors and evaluate the impact of the road network on species migration; the ecosystem service value index is used to evaluate the changes in the ecological service value of the road network based on land use and ecological functions; the carbon sequestration capacity assessment index is used to calculate the changes in the carbon sequestration capacity of the road network area; the biodiversity impact index is used to evaluate the impact of the road network on biodiversity based on species richness, distribution of endemic species and the number of endangered species.

[0022] Furthermore, in S4, experts first score the influence of the ecosystem according to each ecological impact assessment indicator to obtain the expert scoring results; then, the average score of each ecological impact assessment indicator is calculated according to the expert scoring results, and the weight of each indicator is obtained through standardization; finally, according to the weight of each indicator, it is weighted and summed with the actual calculated indicator value to obtain the comprehensive evaluation index of the highway network ecosystem.

[0023] Furthermore, there are m ecological impact assessment indicators, and n experts score each ecological impact assessment indicator. Let expert j’s score for the i-th indicator be S ij , then the average score of the i-th indicator for:

[0024]

[0025] The weight value of each indicator is obtained through standardization, and the weight of each indicator is W i for:

[0026]

[0027] in, is the average score of the kth indicator, and the value of k ranges from 1 to m; To ensure that the sum of the weights of all indicators is 1;

[0028] According to the weight of each indicator, the weighted sum is taken with the actual calculated indicator value to calculate the ecosystem comprehensive evaluation index ECI:

[0029]

[0030] Among them, w i is the weight of the i-th indicator, I i is the actual value of the ith indicator, and n is the total number of indicators.

[0031] Furthermore, the ecological impact levels of the road network are classified into the following: very high: 0.8≤ECI≤1.00; high: 0.60≤ECI<0.80; medium: 0.40≤ECI<0.60; low: 0.20≤ECI<0.40; very low: 0.10≤ECI<0.20.

[0032] Furthermore, in S5, the ecological optimization suggestions include: ecological compensation, ecological buffer zone planning, biological corridor optimization and green infrastructure construction.

[0033] Furthermore, the method further includes: using visual analysis to display the ecological impact distribution of the highway network in real time, including: real-time highway network construction and operation data, dynamically updating ecological impact assessment results, and presenting various assessment indicators in a graphical manner.

[0034] Another embodiment of the present invention provides a highway network ecosystem evaluation system, including: a data acquisition module, a data preprocessing and integration module, an ecological impact calculation module, a decision support module and a visualization analysis module, wherein the data acquisition module is used to obtain highway network data and ecological environment data; the data preprocessing and integration module is used to perform data preprocessing and data integration on the highway network data and ecological environment data to obtain integrated highway network data and ecological environment data, and integrate all collected data to form ecological environment impact assessment data; the ecological impact calculation module constructs an ecological impact assessment index system, and the ecological impact assessment index system includes: landscape fragmentation index, habitat suitability index, ecological channel connectivity index, ecosystem service value index, carbon sequestration capacity assessment index and biodiversity impact index; the spatial information of highway network data and ecological environment data is extracted from the ecological environment impact assessment database, and quantitative calculation is performed in combination with the ecological impact assessment index system, using depth The learning model performs automatic analysis and prediction of data to obtain the impact value of each indicator on the ecological environment, compares the calculated results of each indicator with the constructed ecological impact evaluation index system, substitutes the extracted spatial information into the calculation of each indicator, and obtains the corresponding quantitative results of each indicator to achieve quantitative calculation of the impact on the ecological environment; adopts the expert scoring method to calculate the weight of each ecological impact evaluation indicator, and according to the weight of each indicator, performs weighted summation with the actual calculated indicator value to obtain the comprehensive evaluation index of the highway network ecosystem; the decision support module is used to divide the ecological impact level of the highway network according to the comprehensive evaluation index of the highway network ecosystem, and generate an ecological impact evaluation report; and provide ecological optimization suggestions according to the ecological impact level; the visualization analysis module is used to use visualization analysis to display the ecological impact distribution of the highway network in real time, including: real-time highway network construction and operation data, dynamic update of ecological impact assessment results, and graphical presentation of various evaluation indicators.

[0035] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0036] 1. The highway network ecosystem evaluation method provided by the present invention can comprehensively consider multi-dimensional ecological and environmental data, adopt a scientific evaluation system and expert scoring method, and objectively and accurately evaluate the impact of the highway network on the ecological environment.

[0037] 2. The visualization platform of the present invention can display the ecological impact of the highway network in real time, provide intuitive reference information for decision makers, and guide subsequent ecological protection and optimization measures.

[0038] 3. Through precise weighted calculation, the present invention can quantify the impact of the highway network on the ecological environment, providing a scientific basis for formulating measures such as ecological compensation and green infrastructure construction.

[0039] 4. The ecological impact assessment method of the present invention can provide personalized assessment results for different types of highway networks, providing strong support for the planning, design and operation management of highway networks.

[0040] 5. The present invention provides technical service support for highway network ecological assessment by using data information technology processing means such as data collection and data preprocessing on highway network data and ecological environment data.

[0041] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments with reference to the following drawings, in which:

[0043] Figure 1 is a flow chart of a road network ecosystem evaluation method according to an embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of a road network ecosystem evaluation method according to an embodiment of the present invention;

[0045] Figures 3a to 3d A spatial distribution map of ecosystem evaluation indicators according to an embodiment of the present invention;

[0046] Figure 4 is a diagram showing evaluation results of a highway network ecosystem according to an embodiment of the present invention;

[0047] Figure 5 A system visualization diagram according to an embodiment of the present invention;

[0048] Figures 6a to 6g This is an interface diagram of a statistical analysis report according to an embodiment of the present invention;

[0049] Figure 7 4 is a structural diagram of a highway network ecosystem evaluation system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0051] This paper proposes a road network ecosystem assessment method, based on multi-index analysis and expert scoring, and a visualization platform for its quality assessment. This method comprehensively considers the impact of the road network on the ecological environment, encompassing data from multiple dimensions such as climate, land use, water resources, biodiversity, and pollution. Through quantitative calculations using ecological models, it conducts an in-depth analysis of the relationship between road network construction and the ecological environment, providing decision-making support for ecological protection.

[0052] The highway network ecosystem evaluation method provided by the present invention is a weight assignment and weighted calculation method based on the expert scoring method, which provides a new solution for highway network ecosystem evaluation. The expert scoring method can assign weights to the relative importance of multiple evaluation indicators based on the experience and knowledge of experts in the field, and then obtain a comprehensive ecological impact evaluation result through weighted calculation. This method can effectively integrate the influence of various ecological and environmental factors, making the evaluation results more scientific and reasonable. In addition, the application of the decision support system to the field of ecological protection can provide optimized ecological protection measures such as ecological compensation, ecological buffer zone planning, and biological corridor optimization in combination with the results of the ecological impact assessment. These measures are of great practical significance for alleviating the negative impact of the highway network on the ecological environment. The highway network ecosystem evaluation method and system provided by the present invention provide technical service support for highway network ecological assessment by performing a series of data information technology processing means (for example, data collection and data preprocessing, etc.) on highway network data and ecological and environmental data.

[0053] The present invention provides a comprehensive and systematic highway network ecological environment impact assessment framework by integrating multiple data sources and adopting a weighted comprehensive scoring method.

[0054] like Figure 1 and Figure 2 As shown, the road network ecosystem evaluation method according to the embodiment of the present invention includes the following steps:

[0055] S1, obtain road network data and ecological environment data, and perform data preprocessing and data integration on the road network data and ecological environment data to obtain integrated road network data and ecological environment data, integrate all collected data to form an ecological environment impact assessment database.

[0056] Specifically, this step involves data collection, which is the foundation of the entire highway network ecological and environmental impact assessment system. The accuracy and effectiveness of the system depend on the comprehensiveness, timeliness, and accuracy of the collected data. To ensure the scientific nature and accuracy of the assessment results, the present invention utilizes a variety of methods and techniques to collect and process various types of data, including basic map data, climate data, and ecological and environmental data. The data collection process begins with obtaining highway network and ecological and environmental data, and then undergoing necessary data preprocessing.

[0057] 1) Highway network data

[0058] In the present invention, the highway network data needs to be extracted and formatted using GIS technology, and different types of highways need to be classified and numbered according to hierarchical relationships.

[0059] Highway network data includes information such as the spatial distribution of the highway network, road network density, road section characteristics (such as road surface type, grade, width, and capacity), and traffic flow. For example, information includes highway type (such as expressway, national highway, provincial highway, county highway, and township highway); road width, road surface material, construction year, and designed traffic flow; road slope, curvature, and elevation changes; and traffic flow, vehicle speed, and traffic density.

[0060] The following is an explanation of the specific project of highway network data collection:

[0061] Road network data is one of the core components of this assessment system. It is necessary to accurately obtain all road data in the target area, including but not limited to urban roads, highways, rural roads, and related traffic flow information. Specific collection methods include the following:

[0062] 1. Remote Sensing Image Acquisition: Utilize high-resolution satellite remote sensing imagery (such as Landsat and Sentinel) to obtain road distribution information for the target area. Image classification techniques distinguish road areas from other land features (such as green spaces, buildings, and water bodies) to accurately extract road network data. The accuracy of this process directly impacts the subsequent ecological impact assessment.

[0063] In order to improve the accuracy of road extraction, the present invention uses an image classification method based on deep learning to process images.

[0064] (1) Image preprocessing: Perform radiometric correction, atmospheric correction, geometric correction, and cropping on remote sensing images to improve data quality and classification accuracy.

[0065] (2) Sample construction and annotation: Based on existing map data or manual annotation, a training sample set containing typical land feature categories such as "road", "green space", "building", and "water body" is constructed as the input of the image classification model.

[0066] (3) Classification model selection and training: Deep learning models such as convolutional neural networks (CNNs) are used for image classification. Optional models include, but are not limited to, U-Net, DeepLabv3+, and SegNet. During model training, pixel blocks of remote sensing images are input, and the corresponding ground feature category label for each pixel is output.

[0067] (4) Road extraction and refinement: From the classified image output by the model, pixel regions belonging to the “road” category are extracted to form a preliminary road distribution map. Morphological processing (such as erosion, dilation, and refinement) and edge vectorization are then used to further enhance the clarity of road boundaries and the integrity of the network structure.

[0068] (5) Accuracy assessment: The extracted results are compared with manual vector data or existing authoritative road data, and the accuracy is assessed using indicators such as overall accuracy and Kappa coefficient. This process can effectively improve the accuracy of road extraction, especially in areas with high vegetation coverage and complex road backgrounds, thereby ensuring the accuracy and reliability of the spatial characteristic information of the road network in subsequent ecological impact assessments.

[0069] 2. Build a road spatial database: Integrate road spatial data released by national or local governments to obtain detailed road network structure data, including road grade, width, number of lanes, traffic flow, and other information. For example, you can use road data provided by the China National Geographic Information Public Service Platform or urban road information released by local governments. Connect to the road spatial database through a data interface to ensure that the collected road data is up-to-date and has spatial attributes to support subsequent spatial analysis.

[0070] 3. Obtain GPS and traffic flow data: Combined with road traffic flow information, collect data on traffic volume, speed, and density for different road types. Use public data from government traffic management departments, or use traffic sensors and GPS tracking devices to collect dynamic traffic data. This data will help assess the degree of environmental disruption caused by different road traffic flows when calculating ecological impacts.

[0071] 4. Road Network Topology Analysis: Using spatial analysis techniques, we conduct a topological analysis of the road network to obtain information such as its connectivity and the relative position of road sections. This data is particularly important for the subsequent calculation of the Landscape Fragmentation Index (LSI), which helps determine the extent of the road network's impact on the surrounding ecological landscape.

[0072] (2) Ecological and environmental data

[0073] In the embodiment of the present invention, the ecological and environmental data includes multiple dimensions such as climate data, land use data, water resources data, biodiversity data, and pollution data. These data can be collected through various means such as remote sensing technology, geographic information system (GIS) technology, and traffic monitoring systems.

[0074] The above ecological environment data includes the following subcategories:

[0075] Climate data includes: temperature, humidity, precipitation, wind speed, air pressure, etc.

[0076] Land use data include: forests, grasslands, cultivated land, wetlands, bare land, vegetation coverage, land types, water bodies, forests, agricultural land, urban land, and spatial distribution information of land types.

[0077] Water resource data include hydrological information such as rivers, lakes, wetlands, groundwater recharge areas, water body area, and river basin distribution.

[0078] Biodiversity data includes: distribution of animal and plant species, migration routes, habitats of endangered species, ecological protection areas, etc.

[0079] Pollution data include: air pollution, water pollution, soil pollution, noise pollution, air quality, soil pollution and other data.

[0080] Ecological and environmental data also require spatial registration and standardization to ensure consistency across different data sources. Remote sensing imagery data undergoes radiometric and atmospheric correction to improve accuracy and reliability. For ground-based monitoring data, statistical methods are used to fill in missing values and remove outliers.

[0081] The following is an explanation of the climate data collection process:

[0082] The collection of climate data is another key link in this system because climate change is an important factor affecting the ecological environment.

[0083] The present invention collects the required climate data in the following manner:

[0084] 1. Meteorological Data Collection: Using meteorological data provided by the national meteorological authorities, we collect historical data on climate elements such as temperature, precipitation, wind speed, and humidity for the target area. In particular, we focus on factors such as temperature and precipitation changes, as these are crucial for assessing the ecological environment, particularly habitat suitability (HSI). For example, the system uses an API to obtain high-precision daily meteorological data, including temperature, precipitation, and wind speed, from meteorological service platforms such as the China Meteorological Administration.

[0085] 2. Remote sensing climate data: Satellite remote sensing data (such as MODIS) is used to obtain a wider range of climate information, especially surface temperature data. MODIS data has the advantages of high spatial resolution and wide coverage, providing temperature trends over a wide range, helping to study the impact of regional climate change on the ecological environment.

[0086] 3. Climate Model Prediction: Future climate change predictions are made using existing climate models (such as RCP4.5 and RCP8.5 scenarios). Climate simulation software is used to predict climate change trends over the next few decades. The long-term impacts of climate change need to be considered, particularly in the context of highway network construction and expansion, to enable dynamic assessments of indicators such as habitat suitability and carbon sequestration capacity.

[0087] 4. Seasonal Climate Change Data: In addition to long-term climate change data, seasonal climate change (such as spring temperature fluctuations and autumn and winter precipitation changes) is also a key focus of this system. By collecting real-time meteorological data and analyzing historical climate data, the system can understand the changing patterns of a region's seasonal climate, providing more accurate spatiotemporal information for subsequent analysis.

[0088] The following is an explanation of the ecological environment data collection process.

[0089] Ecological and environmental data is one of the core data for highway network ecological and environmental impact assessment. This type of data involves information on biodiversity, habitat distribution, vegetation cover, and other aspects.

[0090] The present invention collects ecological environment data in the following ways:

[0091] 1. Vegetation Cover Data: Vegetation cover in target areas is monitored using remote sensing imagery and vegetation indices (such as NDVI). Remote sensing imagery can help assess vegetation distribution and the spatial distribution patterns of different vegetation types, and can also calculate information such as vegetation growth status and density. The NDVI index, a commonly used indicator for measuring vegetation greenness, accurately reflects changes in vegetation cover and is crucial for calculating carbon sequestration capacity (CHC) and landscape fragmentation index (LSI).

[0092] 2. Species distribution data: Species distribution data is collected through biological surveys, ecological and environmental monitoring, and species observation databases. By combining data from biologists and research institutions, the system can obtain information on the habitats of specific species. This data can help the system assess the impact of road construction on habitat suitability, particularly potential threats to the habitats of endangered or endemic species.

[0093] 3. Habitat type data: By combining ecological surveys, climate data, and remote sensing data, the spatial distribution of habitat types is assessed. For example, different types of habitats, such as wetlands, forests, and grasslands, require separate data collection. In the case of Beijing, the system extracted different types of habitat areas by combining remote sensing images with ground survey data, and provided basic data for habitat suitability (HSI) assessment.

[0094] 4. Water Resources and Waterbody Distribution Data: To assess water conservation capacity (WCC), the system collects waterbody distribution data, including the spatial location, drainage basin extent, and waterbody type of surface water bodies (such as rivers, lakes, and reservoirs). Using remote sensing imagery and data provided by water conservancy departments, the system can capture information on the spatial distribution and area changes of water bodies.

[0095] The various data collected usually need to be pre-processed and integrated before they can be put into the evaluation system. After obtaining the road network data and ecological environment data, the above data are pre-processed and integrated, including:

[0096] 1. Data cleaning

[0097] Data cleaning is performed on the collected original road network data and ecological environment data to remove invalid or duplicate data. The quality of the data is ensured by checking for missing values, abnormal values, etc.

[0098] 2. Data space alignment and matching

[0099] Spatial alignment was performed across datasets to ensure consistency in spatial resolution and coordinate system across all datasets. All vector and raster data was coordinate-normalized and reprojected to EPSG:4326 using the rasterio tool. For remote sensing imagery with varying spatial resolutions, common algorithms such as bilinear interpolation were used to uniformly resample to a specified 30-meter resolution. Reprojection was used to align multiple raster data sets to a unified template grid, ensuring consistency in spatial boundaries, resolution, and projection.

[0100] 3. Data format conversion:

[0101] Data formats may differ from source to source, necessitating data conversion. GIS data is typically in Shapefile or GeoJSON format, meteorological data in CSV or NetCDF, and remote sensing imagery in GeoTIFF. The system uses specialized scripts to convert this data into a standardized GeoJSON format. The system automatically identifies the input data format type and categorizes it based on the file extension or header information. For example, vector data is uniformly converted to GeoJSON format, facilitating its matching with raster data in subsequent spatial analysis, ensuring compatibility.

[0102] 4. Data Integration

[0103] All collected data is integrated through a GIS platform or spatial database to form a complete ecological and environmental impact assessment database. This database contains road network data, climate data, ecological and environmental data, and other relevant data, all of which can be spatially queried and analyzed.

[0104] S2. Construct an ecological impact assessment indicator system.

[0105] This step quantifies the ecological impact of highway network construction. This involves constructing an ecological impact assessment indicator system based on remote sensing data and GIS analysis. This system not only includes traditional indicators such as landscape fragmentation, habitat suitability, and ecological corridor connectivity, but also incorporates new dimensions such as carbon sequestration capacity and ecological service value, tailored to specific environmental conditions. The construction of these indicators relies on environmental ecological models and remote sensing technology, enabling regionalized calculations.

[0106] The ecological impact assessment index system includes at least one or more of the following indicators: landscape fragmentation index, habitat suitability index, ecological channel connectivity index, ecosystem service value index, carbon sequestration capacity assessment index and biodiversity impact index, etc.

[0107] (1) Landscape fragmentation index (LSI)

[0108] The Landscape Fragmentation Index measures the extent to which road networks are damaging pristine ecological landscapes. It assesses the degree to which road network construction fragments the surrounding landscape and reflects the impact of road construction on ecological and environmental connectivity.

[0109] The formula for the landscape fragmentation index is:

[0110] The Landscape Fragmentation Index (LFI) assesses the degree of fragmentation of the surrounding landscape caused by highway network construction, reflecting the impact of highway construction on ecological and environmental connectivity. A represents the landscape area, and P represents the perimeter of the landscape boundary. Larger values indicate more fragmented landscapes.

[0111] (2) Habitat Suitability Index (HSI)

[0112] The habitat suitability index is based on habitat model analysis and evaluates the impact of the road network on the living environment of surrounding animals and plants (species habitat suitability).

[0113] The Habitat Suitability Index (HSI) is a weighted calculation formula based on a habitat model, typically employing a species habitat suitability assessment model (MaxEnt or the Habitat Accessibility Model). The HSI assesses the impact of highway construction on species habitat suitability through habitat model analysis. Its calculation method relies on the species' environmental requirements and changes in habitat before and after highway construction. Higher HSI values indicate a lower impact of highway construction on habitat suitability.

[0114] (3) Ecological Channel Connectivity Index (ECCI)

[0115] The ecological corridor connectivity index is used to calculate the integrity of ecological corridors and evaluate the impact of road networks on species migration. It measures the impact of road networks on the connectivity of ecological corridors and whether the road network blocks the migration or passage of species.

[0116] The calculation formula of the ecological channel connectivity index is:

[0117] Road networks can block ecological corridors, affecting species migration and movement. This formula calculates the proportion of ecological corridors to the total traversable area; a higher proportion indicates better ecological corridor connectivity.

[0118] (4) Ecosystem Service Value Index (ESVI)

[0119] The Ecosystem Service Value Index (ESVI) assesses changes in the ecological service value of road networks based on land use and ecological function. Using remote sensing data and ecological models, the ESVI quantifies the extent to which ecosystem services (such as water conservation, climate regulation, and carbon storage) are affected by road networks.

[0120] The ecosystem service value index is a comprehensive index based on remote sensing data and ecological models. It is usually calculated using the weighted average method, comprehensively considering the impact of multiple ecological services such as water conservation, climate regulation, and carbon storage.

[0121] The Ecosystem Service Value Index quantifies the extent to which ecosystem services (such as water conservation, climate regulation, and carbon storage) are affected by highway network construction. Its calculation is based on remote sensing imagery, climate models, and ecological models, and assesses the negative impact of highways on environmental services.

[0122] (5) Carbon Sequestration Capacity Assessment Index (CHC)

[0123] The carbon sequestration capacity assessment index is used to calculate changes in the carbon sequestration capacity of the highway network region, assess the impact of highway construction on the carbon storage capacity of surrounding vegetation, and thus quantify changes in carbon emissions.

[0124] The calculation formula of carbon sequestration capacity assessment index is: CHC = vegetation carbon storage capacity - road impact loss

[0125] The carbon sequestration capacity assessment index is used to assess the impact of highway construction on the carbon storage capacity of surrounding vegetation, thereby quantifying changes in carbon emissions. The formula calculates the difference in carbon storage capacity before and after highway construction, with larger values indicating a smaller impact on carbon storage capacity.

[0126] (6) Biodiversity Impact Index (BDI)

[0127] The Biodiversity Impact Index is used to assess the impact of road networks on biodiversity based on species richness, distribution of endemic species and number of endangered species.

[0128] Biodiversity impact indices are based on biodiversity models and are typically calculated through assessments of species distribution changes, habitat destruction, and migration barriers.

[0129] The Biodiversity Impact Index (BII) assesses the impact of road network construction on local biodiversity, including habitat destruction and species migration barriers. The index is calculated based on changes in species habitats, the destruction of ecological corridors, and the obstruction of species migration. Higher values indicate greater impacts on biodiversity.

[0130] S3, extracts the spatial information of road network data and ecological environment data from the ecological environment impact assessment database, and combines it with the ecological impact assessment index system for quantitative calculation. Uses a deep learning model to automatically analyze and predict the data to obtain the impact value of each indicator on the ecological environment. The calculated results of each indicator are compared with the constructed ecological impact assessment index system. The extracted spatial information is substituted into the calculation of each indicator to obtain the corresponding quantitative results of each indicator, so as to achieve quantitative calculation of the impact on the ecological environment.

[0131] In this step, the impact of highway network construction on the ecological environment is quantified based on remote sensing data, geographic information systems (GIS), ecological simulation models, and machine learning algorithms. The calculated results are compared with the evaluation index system to determine the ecological impact level of the highway network and generate an ecological impact assessment report.

[0132] Comprehensive calculations are performed using technologies based on remote sensing data, GIS analysis, deep learning, and ecological models.

[0133] In an embodiment of the present invention, the ecological model includes but is not limited to:

[0134] Species distribution model (SDM); landscape connectivity model (Circuit Theory, Least-cost Path Analysis); carbon sink calculation model (InVEST model, CASA model); land use change simulation model (CA-Markov model, CLUE-S model), etc.

[0135] This paper uses deep learning technology and ecological models to quantify the impact that highway network construction may have on the ecological environment.

[0136] Specifically, remote sensing data (such as satellite imagery) and GIS technology are used to extract spatial information of the road network and its surrounding ecological environment, and quantitative calculations are performed in combination with the established ecological impact assessment index system. A deep learning model is used to automatically analyze and predict the data to obtain the impact value of each indicator on the ecological environment. Subsequently, the calculated results of each indicator are compared with the constructed ecological impact assessment index system, and the above spatial information is substituted into the specific calculation formula of each indicator to obtain the corresponding quantitative results of each indicator. Each indicator corresponds to an ecological impact factor, and its calculation method has been clearly specified in the above index system.

[0137] For example, the vegetation cover change rate is calculated through the change of NDVI (normalized difference vegetation index); finally, the quantitative results of each ecological impact indicator are output, and comprehensive ecological impact assessment data is formed for subsequent comprehensive evaluation and decision support.

[0138] After data collection is complete, the various data points collected are processed to calculate a series of ecological and environmental impact indicators. These indicators can comprehensively reflect the multifaceted impacts of highway network construction on the ecological environment. The calculation methods for each indicator are explained below.

[0139] 1. Landscape Shape Index (LSI)

[0140] Landscape fragmentation is a key indicator to measure the impact of road construction on the surrounding ecological landscape structure, especially how it affects the continuity of the ecological environment and the connectivity of biological habitats during road expansion.

[0141] The calculation method of landscape fragmentation is as follows:

[0142] Data input: road network data, green space coverage data. The spatial distribution information of roads and urban green space is obtained through remote sensing images and land use data.

[0143] Calculation method: Based on the landscape analysis tool (Fragstats software), the spatial relationship between road data and green space data is used to calculate the degree of fragmentation of the surrounding ecological landscape caused by road construction. The process includes the following:

[0144] (1) Landscape unit division: Divide the study area into different landscape units, such as green space, forest, wetland, etc., and calibrate the shape and area of each unit.

[0145] (2) Road impact assessment: By calculating the impact of roads on surrounding green landscape units, the contribution of each road segment to landscape fragmentation is obtained. Parameters such as road width, length, and type are taken into account during the calculation process.

[0146] (3) LSI index calculation: The LSI value of the entire area is calculated by weighted average method. The higher the LSI value, the greater the impact of the road on the landscape fragmentation. The calculation formula is as follows:

[0147]

[0148] Where A is the landscape area and P is the perimeter of the landscape boundary. A larger value indicates a more fragmented landscape.

[0149] By calculating the LSI value, the system can draw a spatial distribution map of ecological landscape fragmentation in the region (such as Figure 3a This provides a basis for further ecological impact assessment.

[0150] 2. Habitat Suitability Index (HSI)

[0151] Habitat suitability assessments measure the impact of road construction on habitats, specifically the suitability of habitats for species. By combining climate data with biodiversity distribution models, the system can quantify the impact of road construction on habitats.

[0152] Data input: climate data (such as temperature, precipitation, etc.), biological distribution model (such as species distribution data), road distribution data.

[0153] Calculation method: Based on the combination of climate data and species distribution models, habitat suitability is calculated using the following steps:

[0154] (1) Constructing a climate suitability model: By combining the existing biological characteristics of the species with climate data (such as average annual temperature and precipitation), a climate suitability model is constructed. For example, the MaxEnt (maximum entropy model) is used to model the climate suitability of the target species.

[0155] (2) Habitat suitability score: Based on the matching degree between climate data and species distribution model, a habitat suitability score (HSI) is calculated for each area. This score reflects the suitability of the target area for species habitat.

[0156] (3) Impact of road construction: Assess the impact of road construction on habitat suitability, especially factors such as temperature and precipitation changes caused by roads. The system predicts the potential impact of road construction on habitat suitability by simulating climate change under different scenarios.

[0157] Based on the calculation results, the system generates a spatial distribution map of habitat suitability (such as Figure 3b This map shows how habitat suitability varies across regions, helping researchers identify habitat areas most affected by road construction.

[0158] 3. Carbon Holding Capacity (CHC)

[0159] Carbon sequestration capacity is used to assess the impact of road construction on a region's carbon storage and absorption capacity. This indicator is calculated primarily based on remote sensing imagery and vegetation cover data. By monitoring changes in carbon storage in target areas, the impact of road construction on carbon sequestration capacity is assessed.

[0160] Data input: remote sensing image data, vegetation cover data, soil type and biomass data.

[0161] Calculation method: The calculation process of carbon sequestration capacity includes the following steps:

[0162] (1) Vegetation Cover Assessment: Use NDVI (Normalized Difference Vegetation Index) data from remote sensing images to assess the vegetation coverage within the area. NDVI values can reflect the health and biomass of vegetation.

[0163] (2) Calculation of carbon storage: Combining vegetation type and biomass data, the carbon storage capacity of different regions is calculated through the carbon storage factor (carbon storage per hectare of vegetation biomass).

[0164] (3) Road impact assessment: Analyze the impact of road construction on vegetation cover and biomass. Road expansion usually leads to vegetation destruction and reduced carbon storage. Therefore, the system adjusts the carbon storage capacity assessment results based on changes in road location and area.

[0165] like Figure 3c As shown, the calculated spatial distribution map of carbon sequestration capacity shows the impact of road construction on the carbon storage capacity of different regions, helping decision makers understand the long-term impact of road construction on regional carbon emissions and carbon absorption.

[0166] 4. Water Conservation Capacity (WCC)

[0167] The water conservation capacity assessment measures the impact of road construction on regional water resource protection and water body distribution. This metric is similar to carbon sequestration capacity, but focuses on changes in water body distribution and water conservation capacity.

[0168] Data input: water body distribution data, vegetation cover data, and land use type data.

[0169] Calculation method: The calculation method of water conservation capacity includes the following steps:

[0170] (1) Water body distribution analysis: Analyze the spatial distribution and area changes of water bodies in the target area using remote sensing images and ground water body distribution data.

[0171] (2) Relationship between vegetation coverage and water conservation capacity: The impact of vegetation destruction on water conservation capacity was evaluated through correlation analysis between vegetation coverage and water conservation capacity.

[0172] (3) Road impact assessment: Analyze the impact of road construction on water conservation capacity, especially the potential impact of road construction on water flow, permeability and water supply.

[0173] like Figure 3d As shown in the figure, the system generates a spatial distribution map of water conservation capacity, which shows the changes in water conservation capacity in different regions and helps researchers identify water source areas affected by road construction.

[0174] S4, use the expert scoring method to calculate the weight of each ecological impact assessment indicator, and according to the weight of each indicator, perform weighted summation with the actual calculated indicator value to obtain the comprehensive evaluation index of the highway network ecosystem.

[0175] Based on the quantitative calculation results of ecological impact, the present invention further introduces an expert scoring method. By inviting multiple field experts to score each ecological impact assessment indicator, the weight of each indicator is calculated based on the expert scoring results. The expert scoring criteria are determined based on the significance of the ecological impact, the weight of the ecological factors, and the characteristics of different regions, ensuring that the weight assigned to each ecological impact indicator fully reflects its actual role in the highway network ecological impact assessment. Through a weighted calculation method, the assessment results of each influencing factor are integrated into the final ecological impact assessment result and standardized.

[0176] First, experts score the influence of the ecosystem according to each ecological impact assessment indicator to obtain the expert scoring results.

[0177] Specifically, experts from various fields were invited to rate the importance of ecosystems based on each ecological impact assessment indicator. Based on their experience and expertise, the experts rated the impact of each indicator on a scale of 1 to 5, with 5 indicating the greatest impact and 1 the least. For example, experts based their ratings on the significance of the ecological impact and the weighting of ecological factors.

[0178] Then, the average score of each ecological impact assessment indicator is calculated based on the expert scoring results, and the weight of each indicator is obtained through standardization.

[0179] Specifically, statistical analysis is conducted on the expert scoring results, and the weight of each indicator is calculated using the weighted average method.

[0180] Finally, according to the weight of each indicator, the weighted sum is performed with the actual calculated indicator value to obtain the comprehensive evaluation index of the highway network ecosystem.

[0181] Suppose there are m ecological impact assessment indicators, and n experts score each ecological impact assessment indicator. Let expert j’s score for the i-th indicator be S ij , then the average score of the i-th indicator for:

[0182]

[0183] The weight value of each indicator is obtained through standardization, and the weight of each indicator is W i for:

[0184]

[0185] in, is the average score of the kth indicator, and the value of k ranges from 1 to m; To ensure that the sum of the weights of all indicators is 1.

[0186] Weighted comprehensive evaluation: Based on the weight of each indicator, the weighted sum is added to the actual calculated indicator value to calculate the ecosystem comprehensive evaluation index ECI:

[0187]

[0188] Among them, w i is the weight of the i-th indicator, I i is the actual value of the ith indicator, and n is the total number of indicators.

[0189] The specific process of expert scoring is as follows:

[0190] Selection of experts: Experts with extensive experience in ecology, environmental science, transportation engineering, climatology and other related fields will be selected to form a scoring panel.

[0191] Setting scoring criteria: Based on the specific impact scope and role of each indicator, the expert group will discuss and determine the impact assessment criteria for each indicator through unified standards before scoring.

[0192] Scoring and review: Experts score each indicator according to the standards. The scoring results are reviewed and statistically analyzed to ultimately determine the weight of each indicator.

[0193] S5: Based on the comprehensive evaluation index of the highway network ecosystem, the ecological impact level of the highway network is divided and an ecological impact assessment report is generated; and ecological optimization suggestions are provided based on the ecological impact level.

[0194] According to the comprehensive evaluation results, the ecological impact of the highway network is divided into five levels: very low, low, medium, high, and very high. In this invention, the classification standards are shown in Table 1.

[0195] Table 1 Grading standards

[0196]

[0197]

[0198] Very low impact zone: The highway has basically no impact on the ecological environment, and the ecosystem functions remain stable.

[0199] Low impact zone: The highway has little impact on the ecological environment, and the ecological function is slightly reduced.

[0200] Moderate impact area: The highway has a certain impact on the ecological environment and requires appropriate ecological restoration measures.

[0201] High impact area: The highway causes significant damage to the ecosystem and requires strict ecological compensation and restoration measures.

[0202] Extremely high impact area: Highways cause serious damage to the ecological environment and may lead to irreversible changes in the ecosystem.

[0203] Each level corresponds to a different degree of ecological and environmental impact, and optimization suggestions are provided based on the specific circumstances. In an embodiment of the present invention, the optimization suggestions include: ecological compensation (such as afforestation, wetland restoration, etc.), ecological buffer zone planning (such as planning green belts on both sides of the highway and establishing ecological barriers), biological corridor optimization (such as building ecological channels, bridges and other facilities), and green infrastructure construction (such as rain gardens, green roofs, etc.). These optimization measures are intended to reduce the negative impact of the road network on the ecological environment and promote the sustainable development of the ecological environment.

[0204] This invention uses a weighted comprehensive scoring method, combining various indicators with different weights to create a final ecological impact score. In this example, the weights of the ecological impact score are set by a team of experts based on local conditions. Using a weighted average calculation method, the system derives the ecological impact level of different regions. Figure 4 The final evaluation results are presented.

[0205] Figure 4 The distribution of ecological impact levels across regions is visually presented through color grading. Light to dark colors represent low to high ecological impact levels, with darker colors indicating more severe ecological disturbance and greater ecosystem sensitivity and vulnerability. This visualization allows for rapid identification of key areas with significant ecological impacts, supporting subsequent ecological restoration, protection planning, and management measures.

[0206] In addition, the present invention further constructs a visualization platform, which uses visualization analysis to display the ecological impact distribution of the highway network in real time, including: real-time highway network construction and operation data, dynamic update of ecological impact assessment results, and presentation of various assessment indicators in a graphical manner.

[0207] Specifically, the present invention establishes a visual analysis platform that displays the ecological impact distribution of the highway network in real time. This platform generates spatial distribution maps and statistical analysis reports, providing intuitive support for ecological protection and optimization decisions. The platform dynamically updates ecological impact assessment results based on real-time highway network construction and operation data, and graphically presents various assessment indicators, facilitating informed decision-making. Figure 5 The interface of the visualization platform is shown. Figures 6a to 6g This is an interface diagram of a statistical analysis report according to an embodiment of the present invention.

[0208] This invention displays assessment results through an interactive visualization platform, allowing users to view the distribution of different indicators and assess the impact of different roads on the ecological environment as needed. The platform supports exporting results into a report format, facilitating subsequent decision-making and analysis by relevant departments.

[0209] like Figure 7 As shown, the highway network ecosystem evaluation system according to the embodiment of the present invention includes: a data acquisition module 100 , a data preprocessing and integration module 200 , an ecological impact calculation module 300 , a decision support module 400 and a visualization analysis module 500 .

[0210] The data acquisition module 100 is used to obtain road network data and ecological environment data.

[0211] The data preprocessing and integration module 200 is used to preprocess and integrate road network data and ecological and environmental data, obtaining integrated road network and ecological and environmental data. All collected data is then integrated to form ecological and environmental impact assessment data. The data preprocessing and integration module can perform data processing such as format conversion, noise removal, and spatial matching.

[0212] The ecological impact calculation module 300 constructs an ecological impact assessment index system, which includes: landscape fragmentation index, habitat suitability index, ecological channel connectivity index, ecosystem service value index, carbon sequestration capacity assessment index and biodiversity impact index; extracts spatial information of road network data and ecological environment data from the ecological environment impact assessment database, and combines it with the ecological impact assessment index system for quantitative calculation, uses a deep learning model to automatically analyze and predict the data, and obtains the impact value of each indicator on the ecological environment. The calculated results of each indicator are compared with the constructed ecological impact assessment index system, and the extracted spatial information is substituted into the calculation of each indicator to obtain the corresponding quantitative results of each indicator, so as to realize quantitative calculation of the impact on the ecological environment; uses the expert scoring method to calculate the weight of each ecological impact assessment indicator, and according to the weight of each indicator, performs weighted summation with the actual calculated indicator value to obtain a comprehensive evaluation index of the road network ecosystem.

[0213] The decision support module 400 is used to classify the ecological impact level of the highway network according to the comprehensive evaluation index of the highway network ecosystem and generate an ecological impact assessment report; and provide ecological optimization suggestions based on the ecological impact level, including ecological compensation, buffer zone planning, and biological corridor optimization.

[0214] The visualization analysis module 500 uses visualization analysis to display the ecological impact distribution of the highway network in real time. This includes real-time highway network construction and operation data, dynamically updated ecological impact assessment results, and graphically presenting various assessment indicators. The visualization analysis module 500 can generate spatial distribution maps, time series graphs, statistical analysis reports, and more.

[0215] In summary, the method and system provided by this invention comprehensively and systematically assess the ecological and environmental impact of highway networks through multiple steps, including data collection, construction of an ecological impact indicator system, quantitative calculation, expert scoring, impact grading, optimization recommendations, and a visualization platform. This method and system can provide a scientific basis for the planning, construction, and management of transportation infrastructure, while also offering effective tools and methods for environmental protection and ecological restoration, and has broad application prospects.

[0216] Those skilled in the art will readily understand that the present invention encompasses any combination of the components described in the Summary and Detailed Description of the Invention and the accompanying drawings. Due to space limitations and for the sake of clarity, not all of the various solutions resulting from these combinations are described. Any modifications, equivalent substitutions, and improvements within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A road network ecosystem evaluation method, characterized in that: The steps include: S1, obtaining road network data and ecological environment data, and performing data preprocessing and data integration on the road network data and ecological environment data to obtain integrated road network data and ecological environment data, and integrating all collected data to form an ecological environment impact assessment database; S2. Constructing an ecological impact assessment indicator system, which includes: landscape fragmentation index, habitat suitability index, ecological corridor connectivity index, ecosystem service value index, carbon sequestration capacity assessment index, and biodiversity impact index; S3, extracting spatial information of highway network data and ecological environment data from the ecological environment impact assessment database, and combining it with the ecological impact assessment index system to perform quantitative calculations, using a deep learning model to automatically analyze and predict the data, obtaining the impact value of each indicator on the ecological environment, comparing the calculated results of each indicator with the constructed ecological impact assessment index system, substituting the extracted spatial information into the calculation of each indicator, and obtaining the corresponding quantitative results of each indicator, thereby achieving quantitative calculation of the impact on the ecological environment; S4, using the expert scoring method to calculate the weight of each ecological impact assessment indicator, and according to the weight of each indicator, perform a weighted summation with the actual calculated indicator value to obtain the comprehensive evaluation index of the highway network ecosystem; S5. Classify the ecological impact level of the highway network according to the comprehensive evaluation index of the highway network ecosystem, generate an ecological impact evaluation report, and provide ecological optimization suggestions based on the ecological impact level.

2. The road network ecosystem evaluation method according to claim 1, characterized in that: The highway network data includes: spatial distribution of highways, road network density, road section characteristics and traffic flow data; The ecological and environmental data include: climate data, land use data, water resources data, biodiversity data and pollution data.

3. The road network ecosystem evaluation method according to claim 1, characterized in that: In S1, data preprocessing and data integration are performed on the road network data and ecological environment data, including: First, the collected road network data and ecological environment data are cleaned to remove invalid or duplicate data; Then perform data spatial alignment and matching and convert the data format into a unified GeoJSON format standard; Finally, all collected data are integrated to form an ecological and environmental impact assessment database.

4. The road network ecosystem evaluation method according to claim 1, characterized in that: The landscape fragmentation index is used to measure the degree of damage caused by the road network to the original ecological landscape; The habitat suitability index is used to assess the impact of the road network on the living environment of surrounding animals and plants; The ecological corridor connectivity index is used to calculate the integrity of ecological corridors and assess the impact of road networks on species migration; The ecosystem service value index is used to assess changes in the ecological service value of the road network based on land use and ecological functions; The carbon sequestration capacity assessment index is used to calculate changes in the carbon sequestration capacity of the highway network area; The biodiversity impact index is used to assess the impact of road networks on biodiversity based on species richness, distribution of endemic species and number of endangered species.

5. The road network ecosystem evaluation method according to claim 1, characterized in that: In S4, experts first score the influence of the ecosystem according to each ecological impact assessment indicator to obtain the expert scoring results; then, the average score of each ecological impact assessment indicator is calculated based on the expert scoring results, and the weight of each indicator is obtained through standardization; finally, according to the weight of each indicator, it is weighted and summed with the actual calculated indicator value to obtain the comprehensive evaluation index of the highway network ecosystem.

6. The road network ecosystem evaluation method according to claim 5, characterized in that: Suppose there are m ecological impact assessment indicators, and n experts score each ecological impact assessment indicator. Let expert j’s score for the i-th indicator be S ij , then the average score of the i-th indicator for: The weight value of each indicator is obtained through standardization, and the weight of each indicator is W i for: in, is the average score of the kth indicator, and the value of k ranges from 1 to m; To ensure that the sum of the weights of all indicators is 1; According to the weight of each indicator, the weighted sum is taken with the actual calculated indicator value to calculate the ecosystem comprehensive evaluation index ECI: Among them, w i is the weight of the i-th indicator, I i is the actual value of the ith indicator, and n is the total number of indicators.

7. The road network ecosystem evaluation method according to claim 6, characterized in that: The ecological impact levels of the road network are as follows: Very high: 0.8 ≤ ECI ≤ 1.00; High: 0.60≤ECI<0.80; Moderate: 0.40≤ECI<0.60; Low: 0.20≤ECI<0.40; Very low: 0.10≤ECI<0.

20.

8. The road network ecosystem evaluation method according to claim 1, characterized in that: In S5, the ecological optimization suggestions include: ecological compensation, ecological buffer zone planning, biological corridor optimization and green infrastructure construction.

9. The road network ecosystem evaluation method according to claim 1, characterized in that: The method further includes: using visual analysis to display the ecological impact distribution of the highway network in real time, including: real-time highway network construction and operation data, dynamically updating ecological impact assessment results, and presenting various assessment indicators in a graphical manner.

10. A highway network ecosystem evaluation system, comprising: Data acquisition module, data preprocessing and integration module, ecological impact calculation module, decision support module and visual analysis module, among which, The data acquisition module is used to obtain road network data and ecological environment data; The data preprocessing and integration module is used to perform data preprocessing and data integration on the road network data and the ecological environment data to obtain integrated road network data and ecological environment data, and integrate all collected data to form ecological environment impact assessment data; The ecological impact calculation module constructs an ecological impact assessment index system, which includes: landscape fragmentation index, habitat suitability index, ecological channel connectivity index, ecosystem service value index, carbon sequestration capacity assessment index and biodiversity impact index; extracts spatial information of road network data and ecological environment data from the ecological environment impact assessment database, and combines it with the ecological impact assessment index system for quantitative calculation, uses a deep learning model to automatically analyze and predict the data, obtains the impact value of each indicator on the ecological environment, compares the calculated results of each indicator with the constructed ecological impact assessment index system, substitutes the extracted spatial information into the calculation of each indicator, and obtains the corresponding quantitative results of each indicator to achieve quantitative calculation of the impact on the ecological environment; uses an expert scoring method to calculate the weight of each ecological impact assessment indicator, and according to the weight of each indicator, performs a weighted summation with the actual calculated indicator value to obtain a comprehensive evaluation index of the road network ecosystem; The decision support module is used to classify the ecological impact level of the highway network according to the comprehensive evaluation index of the highway network ecosystem, and generate an ecological impact evaluation report; and provide ecological optimization suggestions based on the ecological impact level; The visualization analysis module is used to display the ecological impact distribution of the highway network in real time using visualization analysis, including: real-time highway network construction and operation data, dynamically updated ecological impact assessment results, and presenting various assessment indicators in a graphical manner.

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