Bird habitat early warning method based on climate change scene

Abstract

The invention provides a bird habitat early warning method based on a climate change scene, and aims to solve the defects in the aspects of dynamic change and risk early warning of a bird habitat in the prior art. The method comprises the following steps: constructing different climate change scenes based on meteorological data and greenhouse gas emission scenes, and predicting ecological environment changes; simulating bird habitat distribution change by using the species distribution model and the habitat suitability model; setting early warning indexes according to bird inhabitation characteristics; an integrated early warning system is constructed, and risk early warning information is generated through real-time monitoring; the early warning information is displayed through a visual means, and related departments are helped to formulate coping strategies. According to the method, through the intelligent adaptive simulation optimization system, the multi-factor comprehensive evaluation system, the intelligent push strategy system and the interactive query and analysis system, the accuracy, timeliness and effectiveness of early warning are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological environment monitoring, and specifically provides a bird habitat early warning method based on climate change scenarios. Background Art

[0002] The impact of climate change on bird habitats is an interdisciplinary research field involving ecology, meteorology, geographic information systems (GIS), and remote sensing technology. This field focuses on how global warming and climate change affect bird behavior, migration patterns, reproductive success, and the distribution and quality of habitats. With climate-induced changes in vegetation, urbanization, altered nutrient cycles, and shifting migration patterns, bird survival and ecosystems are facing significant threats. Remote sensing technology plays a vital role in bird ecology research, providing detailed information on bird habitats and distribution, helping scientists better understand and predict the impacts of climate change on birds. Bird ecosystems in specific regions, such as the Qinghai-Tibet Plateau, are particularly threatened by climate change, requiring long-term monitoring and research to address these changes.

[0003] A search revealed a method for evaluating a health and wellness climate that combines climate data with social perception data, published under the publication number CN114564517B on October 18, 2024. This product utilizes the collection of historical climate and longitude and latitude information, the calculation of temperature and humidity indices, wind chill index, and vacation index, the construction of a health and wellness climate model and a health and wellness climate social perception model, and the generation of a final geospatial distribution layer. While capable of integrating multi-source data and evaluating health and wellness climate, it primarily focuses on human health and wellness needs and lacks specialized design for dynamic changes in bird habitats and risk warnings.

[0004] Existing technologies for monitoring and providing early warning of climate change impacts on bird habitats suffer from significant shortcomings, including limited data processing capabilities, low prediction accuracy, poor real-time performance, and a lack of dynamic adaptability and comprehensive multi-factor assessment. These limitations hinder accurate monitoring and timely early warning of changes in bird habitats, hindering the effectiveness of bird conservation and management. Therefore, it is necessary to develop a new early warning method to address these issues and improve bird habitat monitoring and early warning capabilities. Summary of the Invention

[0005] The present invention aims to provide a bird habitat early warning system that is more intelligent, accurate and adaptable to climate change, so as to effectively support ecological protection and management.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a bird habitat early warning method based on climate change scenarios, comprising the following steps: S1: Based on meteorological data and greenhouse gas emission scenarios, construct different climate change scenarios and predict ecological and environmental changes under different scenarios: S2: Collect and integrate meteorological data from global climate models (GCMs) and regional climate models (RCMs), including temperature, humidity, precipitation, and wind speed.

[0007] Based on different greenhouse gas emission scenarios (such as RCP2.6, RCP4.5, RCP6.0 and RCP8.5), climate models are used to simulate climate change scenarios at different time points in the future (such as 2030, 2050 and 2080).

[0008] Ecological models (such as dynamic vegetation models and biogeochemical models) are used to predict ecological and environmental changes under different climate change scenarios, including vegetation cover, soil moisture, ecosystem productivity, etc.

[0009] S3: Use species distribution models and habitat suitability models to simulate changes in bird habitat distribution under climate change scenarios: S4: Construct bird species distribution models (such as the maximum entropy model MaxEnt, the niche model Niche) and habitat suitability models (such as the niche suitability model ECOMOD, the habitat suitability model HSM).

[0010] The climate change prediction results are input into the species distribution model and habitat suitability model to simulate the changes in bird habitat distribution under different climate change scenarios.

[0011] Generate spatiotemporal distribution layers of bird habitats, including habitat area, habitat quality, and habitat physical distribution.

[0012] S5: Set early warning indicators based on the habitat characteristics and habitat changes of different bird species, including changes in habitat area and the degree of habitat fragmentation: S6: Collect and organize ecological data of different bird species, including habitat preferences, food chain positions, migration paths, etc.

[0013] Based on the habitat characteristics of birds and simulation results, early warning indicators are set, including changes in habitat area, degree of habitat fragmentation, changes in habitat quality, etc.

[0014] Set a threshold for each warning indicator, and trigger an early warning when the simulation result exceeds the threshold.

[0015] S7: Early warning system implementation: Build an integrated early warning system to generate risk warning information for bird habitats through real-time monitoring of climate and habitat data: S8: Build the early warning system framework, including data acquisition module, data processing module, model calculation module, early warning generation module and information push module.

[0016] The data acquisition module collects meteorological data and habitat data in real time, the data processing module cleans and pre-processes the data, the model calculation module runs the species distribution model and habitat suitability model, the warning generation module generates risk warning information based on warning indicators, and the information push module pushes the warning information to relevant departments and personnel through multiple channels (such as email, text messages, and mobile applications).

[0017] S9: Early warning information display: Display early warning information through visual means, provide intuitive risk levels, and help relevant departments formulate response strategies: S10: Build a visualization platform, including geographic information system (GIS) and visualization tools (such as WebGIS, map engine).

[0018] The warning information is displayed in various forms such as maps, charts, and reports, providing detailed information such as risk level, risk area, and risk date.

[0019] Provide customized visualization interfaces for users at different levels (such as scientists, managers, and the public) to support interactive query and analysis.

[0020] Preferably, the model calculation module also includes an intelligent adaptive simulation optimization system, which includes a dynamic parameter adjustment module, a model adaptive learning module, and a simulation result verification module. The dynamic parameter adjustment module adjusts model parameters based on real-time data to improve simulation accuracy; the model adaptive learning module uses machine learning algorithms (such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs)) to optimize the model structure and improve the model's generalization capabilities; and the simulation result verification module verifies the accuracy of simulation results using historical data and measured data to ensure their reliability and effectiveness.

[0021] Preferably, the early warning generation module also includes a multi-factor comprehensive evaluation system, which includes an evaluation index library, a weight allocation module, and a comprehensive evaluation algorithm. The evaluation index library contains multiple evaluation indicators related to the ecological environment and bird habitats. The weight allocation module assigns weights to each indicator based on expert knowledge and historical data. The comprehensive evaluation algorithm calculates the final early warning level based on the multi-indicator comprehensive evaluation model, thereby improving the scientific nature and accuracy of the early warning.

[0022] Preferably, the information push module also includes an intelligent push strategy system, which includes a user management module, a push rule library, and a push strategy optimization module. The user management module maintains the personalized needs and preferences of different users, the push rule library contains a variety of push rules and conditions, and the push strategy optimization module optimizes the push strategy based on user feedback and push results to improve the timeliness and effectiveness of information push.

[0023] Preferably, the visualization platform also includes an interactive query and analysis system, which includes a spatial query module, a temporal query module, a multidimensional analysis module, and a customized reporting module. The spatial query module supports queries and analysis based on geographic location, the temporal query module supports queries and analysis based on time ranges, the multidimensional analysis module supports multi-dimensional data analysis and visualization, and the customized reporting module supports the generation of customized reports based on user needs, thereby improving the flexibility and practicality of visualization.

[0024] The structural composition, implementation mode and operating principle of the present invention are as follows: Intelligent Adaptive Simulation Optimization System: The dynamic parameter adjustment module adjusts model parameters based on real-time data to improve simulation accuracy. The model adaptive learning module uses machine learning algorithms to optimize model structure and enhance model generalization capabilities. The simulation result verification module verifies the accuracy of simulation results using historical and measured data to ensure their reliability and effectiveness.

[0025] Multi-factor comprehensive evaluation system: The evaluation index library contains multiple evaluation indicators related to the ecological environment and bird habitats. The weight allocation module assigns weights to each indicator based on expert knowledge and historical data. The comprehensive evaluation algorithm calculates the final warning level based on the multi-indicator comprehensive evaluation model to improve the scientificity and accuracy of the warning.

[0026] Intelligent push strategy system: The user management module maintains the personalized needs and preferences of different users. The push rule library contains a variety of push rules and conditions. The push strategy optimization module optimizes the push strategy based on user feedback and push effects to improve the timeliness and effectiveness of information push.

[0027] Interactive query and analysis system: The spatial query module supports query and analysis based on geographic location, the time query module supports query and analysis based on time range, the multidimensional analysis module supports multi-dimensional data analysis and visualization, and the customized report module supports the generation of customized reports based on user needs, improving the flexibility and practicality of visualization.

[0028] Beneficial effects of the present invention: The intelligent adaptive simulation optimization system can dynamically adjust model parameters according to real-time data, optimize the model structure using machine learning algorithms, ensure the accuracy and reliability of simulation results, and significantly improve the simulation accuracy and generalization ability of the model.

[0029] The multi-factor comprehensive assessment system calculates the final warning level through a multi-indicator comprehensive assessment model, which improves the scientific nature and accuracy of the warning and can more comprehensively assess the risks of bird habitats.

[0030] The intelligent push strategy system can optimize push strategies based on user needs and feedback, improve the timeliness and effectiveness of information push, and ensure that relevant departments and personnel can obtain early warning information in a timely manner and formulate response measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0032] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention. Example

[0034] like Figure 1 As shown, a bird habitat early warning method based on climate change scenarios includes the following steps: S1: Based on meteorological data and greenhouse gas emission scenarios, construct different climate change scenarios and predict ecological and environmental changes under different scenarios: S2: Collect and integrate meteorological data from global climate models (GCMs) and regional climate models (RCMs), including temperature, humidity, precipitation, wind speed, etc.

[0035] Based on different greenhouse gas emission scenarios (such as RCP2.6, RCP4.5, RCP6.0 and RCP8.5), climate models are used to simulate climate change scenarios at different time points in the future (such as 2030, 2050 and 2080).

[0036] Ecological models (such as dynamic vegetation models and biogeochemical models) are used to predict ecological and environmental changes under different climate change scenarios, including vegetation cover, soil moisture, ecosystem productivity, etc.

[0037] S3: Use species distribution models and habitat suitability models to simulate changes in bird habitat distribution under climate change scenarios: S4: Construct bird species distribution models (such as the maximum entropy model MaxEnt, the niche model Niche) and habitat suitability models (such as the niche suitability model ECOMOD, the habitat suitability model HSM).

[0038] The climate change prediction results are input into the species distribution model and habitat suitability model to simulate the changes in bird habitat distribution under different climate change scenarios.

[0039] Generate spatiotemporal distribution layers of bird habitats, including habitat area, habitat quality, and habitat physical distribution.

[0040] S5: Set early warning indicators based on the habitat characteristics and habitat changes of different bird species, including changes in habitat area and the degree of habitat fragmentation: S6: Collect and organize ecological data of different bird species, including habitat preferences, food chain positions, migration paths, etc.

[0041] Based on the habitat characteristics of birds and simulation results, early warning indicators are set, including changes in habitat area, degree of habitat fragmentation, changes in habitat quality, etc.

[0042] Set a threshold for each warning indicator, and trigger an early warning when the simulation result exceeds the threshold.

[0043] S7: Early warning system implementation: Build an integrated early warning system to generate risk warning information for bird habitats through real-time monitoring of climate and habitat data: S8: Build the early warning system framework, including data acquisition module, data processing module, model calculation module, early warning generation module and information push module.

[0044] The data acquisition module collects meteorological data and habitat data in real time, the data processing module cleans and pre-processes the data, the model calculation module runs the species distribution model and habitat suitability model, the warning generation module generates risk warning information based on warning indicators, and the information push module pushes the warning information to relevant departments and personnel through multiple channels (such as email, text messages, and mobile applications).

[0045] S9: Early warning information display: Display early warning information through visual means, provide intuitive risk levels, and help relevant departments formulate response strategies: S10: Build a visualization platform, including geographic information system (GIS) and visualization tools (such as WebGIS, map engine).

[0046] The warning information is displayed in various forms such as maps, charts, and reports, providing detailed information such as risk level, risk area, and risk date.

[0047] Provide customized visualization interfaces for users at different levels (such as scientists, managers, and the public) to support interactive query and analysis.

[0048] The model calculation module also includes an intelligent adaptive simulation optimization system, which consists of a dynamic parameter adjustment module, a model adaptive learning module, and a simulation result verification module. The dynamic parameter adjustment module adjusts model parameters based on real-time data to improve simulation accuracy. The model adaptive learning module optimizes the model structure using machine learning algorithms (such as convolutional neural networks (CNNs) and long-short-term memory (LSTMs)) to improve the model's generalization capabilities. The simulation result verification module verifies the accuracy of simulation results using historical and measured data to ensure their reliability and effectiveness.

[0049] The early warning generation module also includes a multi-factor comprehensive assessment system, which consists of an evaluation index library, a weighting module, and a comprehensive assessment algorithm. The evaluation index library contains multiple evaluation indicators related to ecological environment and bird habitats. The weighting module assigns weights to each indicator based on expert knowledge and historical data. The comprehensive assessment algorithm calculates the final early warning level based on the multi-indicator comprehensive assessment model, improving the scientific nature and accuracy of the early warning.

[0050] In this example, three evaluation indicators were selected: habitat area change ( ), habitat fragmentation ( ) and habitat quality changes ( ).

[0051] The weights of these indicators were determined through expert scoring: 、 、 .

[0052] In this embodiment, the index data after the annotation processing is obtained: Changes in habitat area: = 0.8 (indicating a 20% reduction in habitat area); Degree of habitat fragmentation: = 0.6 (indicating a 40% increase in habitat fragmentation); Changes in habitat quality: = 0.7 (indicating a 30% decrease in habitat quality); In this embodiment, the comprehensive evaluation algorithm is as follows: ; Where A is the comprehensive evaluation value; Changes in habitat area; is the degree of habitat fragmentation; for changes in habitat quality; is the weight of habitat area change; is the weight of the degree of habitat fragmentation; is the weight of habitat quality change.

[0053] Change in habitat area ( ), habitat fragmentation ( ) and habitat quality changes ( ); After bringing in the above formula; ; ; In this embodiment, the comprehensive evaluation value A=0.72 indicates that the bird habitat risk in this area is high. In this embodiment, the warning level is divided according to the size of the comprehensive evaluation value: : Low risk; : Medium risk; : High risk; In this embodiment, the comprehensive evaluation value A=0.72 falls in the high-risk range, and is therefore a high-risk warning, which can prompt relevant departments to take emergency protection measures.

[0054] The information push module also includes an intelligent push strategy system, which consists of a user management module, a push rule library, and a push strategy optimization module. The user management module maintains the personalized needs and preferences of different users. The push rule library contains a variety of push rules and conditions. The push strategy optimization module optimizes push strategies based on user feedback and push results, improving the timeliness and effectiveness of information push.

[0055] The visualization platform also includes an interactive query and analysis system, which includes a spatial query module, a temporal query module, a multidimensional analysis module, and a customized reporting module. The spatial query module supports queries and analysis based on geographic location, the temporal query module supports queries and analysis based on time ranges, the multidimensional analysis module supports multi-dimensional data analysis and visualization, and the customized reporting module generates customized reports based on user needs, improving the flexibility and practicality of visualization.

[0056] The structural composition, implementation mode and operating principle of the present invention are as follows: Intelligent Adaptive Simulation Optimization System: The dynamic parameter adjustment module adjusts model parameters based on real-time data to improve simulation accuracy. The model adaptive learning module uses machine learning algorithms to optimize model structure and enhance model generalization capabilities. The simulation result verification module verifies the accuracy of simulation results using historical and measured data to ensure their reliability and effectiveness.

[0057] Multi-factor comprehensive evaluation system: The evaluation index library contains multiple evaluation indicators related to the ecological environment and bird habitats. The weight allocation module assigns weights to each indicator based on expert knowledge and historical data. The comprehensive evaluation algorithm calculates the final warning level based on the multi-indicator comprehensive evaluation model to improve the scientificity and accuracy of the warning.

[0058] Intelligent push strategy system: The user management module maintains the personalized needs and preferences of different users. The push rule library contains a variety of push rules and conditions. The push strategy optimization module optimizes the push strategy based on user feedback and push effects to improve the timeliness and effectiveness of information push.

[0059] Interactive query and analysis system: The spatial query module supports query and analysis based on geographic location, the time query module supports query and analysis based on time range, the multidimensional analysis module supports multi-dimensional data analysis and visualization, and the customized report module supports the generation of customized reports based on user needs, improving the flexibility and practicality of visualization.

[0060] A specific embodiment of a bird habitat early warning method based on climate change scenarios is as follows: In this embodiment, the data acquisition module: the meteorological data acquisition unit collects meteorological data in real time through meteorological data acquisition equipment (in this embodiment, a meteorological station), satellite remote sensing data receiving equipment (in this embodiment, a satellite remote sensing receiver), a ground monitoring station (in this embodiment, a ground meteorological station) and a drone monitoring device (in this embodiment, a sensor mounted on a drone).

[0061] The habitat data collection unit collects bird habitat data in real time through ground monitoring stations (in this embodiment, ground ecological monitoring stations), drone monitoring equipment (in this embodiment, drones equipped with ecological sensors) and other sensor equipment.

[0062] In this embodiment, the data processing module: the data cleaning unit cleans the collected data to remove abnormal values and noise.

[0063] The data preprocessing unit standardizes the cleaned data to ensure the consistency and comparability of the data.

[0064] In this embodiment, the model calculation module: the species distribution model unit constructs a bird species distribution model through the maximum entropy model MaxEnt and the niche model Niche.

[0065] The habitat suitability model unit constructs a bird habitat suitability model through the ecological niche suitability model ECOMOD and the habitat suitability model HSM.

[0066] The maximum entropy model is based on the maximum entropy principle, which states that the model should remain as neutral as possible without violating any known facts, i.e., it selects the one with the highest entropy among all possible distributions. This model can predict the probability of species appearing under different environmental conditions. Niche fitness models consider the location and distribution of species in ecological space. This model can help understand how species interact with their environment and how they adapt to or are affected by changes in the environment. A habitat suitability model (HSM) is a model that assesses the quality of a species' habitat by taking into account the species' specific needs for the habitat, such as food, shelter, and breeding grounds.

[0067] The dynamic parameter adjustment module is responsible for dynamically optimizing model performance based on real-time data. Using a dynamic parameter adjustment algorithm, this module continuously monitors changes in input data and automatically adjusts key model parameters accordingly. This adjustment ensures that the model adapts to the dynamic nature of the data, maintaining or improving its predictive accuracy, response speed, and other performance metrics. The dynamic parameter adjustment algorithm considers the statistical characteristics, trends, and anomalies of the data, calculating the most appropriate parameter settings for the current data characteristics, thereby maintaining the model's optimal performance in a constantly changing data environment.

[0068] The model adaptive learning module optimizes the model structure through convolutional neural network (CNN) and long short-term memory (LSTM) network.

[0069] The simulation result verification module verifies the accuracy of the simulation results through the historical data verification unit and the measured data verification unit.

[0070] In this embodiment, the early warning generation module: the evaluation index library includes multiple evaluation indicators related to the ecological environment and bird habitats.

[0071] The weight allocation module is responsible for allocating corresponding weights to each indicator in the evaluation system based on the preset weight allocation algorithm, combined with expert knowledge and historical data.

[0072] The comprehensive evaluation algorithm calculates the final warning level based on a multi-indicator comprehensive evaluation model.

[0073] In this embodiment, the information push module: the user management module maintains the personalized needs and preferences of different users.

[0074] The push rule library contains various push rules and conditions.

[0075] The push strategy optimization module is responsible for executing the push strategy optimization algorithm, which adjusts and improves the push strategy based on user feedback and push effect data.

[0076] In this embodiment, the visualization platform's spatial query module is designed to handle data query and analysis tasks related to geographic location. This module utilizes spatial query algorithms, enabling users to retrieve and analyze data based on geographic coordinates, regional boundaries, or other spatially relevant criteria. These algorithms support efficient retrieval of spatial data, including but not limited to queries on spatial relationships between points, lines, and surfaces, as well as location-based search and spatial statistical analysis. Through these capabilities, the spatial query module provides precise geographic information, assists decision-making, and enhances understanding of geographic distribution and spatial patterns.

[0077] The time query module supports time range-based query and analysis through time query algorithms.

[0078] The Multidimensional Analysis module specializes in multidimensional data analysis and visualization. Using multidimensional analysis algorithms, it processes and analyzes datasets containing multiple variables or attributes. These algorithms allow users to explore data from different perspectives and levels, identifying trends, patterns, and correlations. The Multidimensional Analysis module supports in-depth statistical analysis, comparisons, and summaries of data, and presents analysis results through charts, graphs, and other visualizations, helping users more intuitively understand the multidimensional characteristics and inherent connections of complex datasets.

[0079] The customized report module generates customized reports according to user needs.

[0080] The specific operation process is as follows: Data collection: Meteorological data collection equipment, satellite remote sensing data receiving equipment, ground monitoring stations and drone monitoring equipment collect meteorological data in real time.

[0081] Ground monitoring stations, drone monitoring equipment and other sensor devices collect bird habitat data in real time.

[0082] Data processing: The data cleaning unit cleans the collected data.

[0083] The data preprocessing unit performs standardization on the cleaned data.

[0084] Model calculation: The species distribution model unit constructs a bird species distribution model through the maximum entropy model MaxEnt and the niche model Niche.

[0085] The habitat suitability model unit constructs a bird habitat suitability model through the ecological niche suitability model ECOMOD and the habitat suitability model HSM.

[0086] The dynamic parameter adjustment module is responsible for dynamically optimizing model performance based on real-time data. Using a dynamic parameter adjustment algorithm, this module continuously monitors changes in input data and automatically adjusts key model parameters accordingly. This adjustment ensures that the model adapts to the dynamic nature of the data, maintaining or improving its predictive accuracy, response speed, and other performance metrics. The dynamic parameter adjustment algorithm considers the statistical characteristics, trends, and anomalies of the data, calculating the most appropriate parameter settings for the current data characteristics, thereby maintaining the model's optimal performance in a constantly changing data environment.

[0087] The model adaptive learning module optimizes the model structure through convolutional neural network (CNN) and long short-term memory network (LSTM).

[0088] The simulation result verification module verifies the accuracy of the simulation results through the historical data verification unit and the measured data verification unit.

[0089] Warning generation: The evaluation index library contains multiple evaluation indicators related to ecological environment and bird habitats.

[0090] The weight allocation module is responsible for allocating corresponding weights to each indicator in the evaluation system based on the preset weight allocation algorithm, combined with expert knowledge and historical data.

[0091] The comprehensive evaluation algorithm calculates the final warning level based on a multi-indicator comprehensive evaluation model.

[0092] Information push: The user management module maintains the personalized needs and preferences of different users.

[0093] The push rule library contains various push rules and conditions.

[0094] The push strategy optimization module is responsible for executing the push strategy optimization algorithm, which adjusts and improves the push strategy based on user feedback and push effect data.

[0095] Visual display: The Spatial Query Module is designed to handle data query and analysis tasks related to geographic location. This module utilizes spatial query algorithms, enabling users to retrieve and analyze data based on geographic coordinates, region boundaries, or other spatially relevant criteria. These algorithms support efficient retrieval of spatial data, including but not limited to queries on spatial relationships between points, lines, and surfaces, as well as location-based search and spatial statistical analysis. Through these capabilities, the Spatial Query Module provides precise geographic information, assists decision-making, and enhances understanding of geographic distribution and spatial patterns.

[0096] The time query module supports time range-based query and analysis through time query algorithms.

[0097] The Multidimensional Analysis module specializes in multidimensional data analysis and visualization. Using multidimensional analysis algorithms, it processes and analyzes datasets containing multiple variables or attributes. These algorithms allow users to explore data from different perspectives and levels, identifying trends, patterns, and correlations. The Multidimensional Analysis module supports in-depth statistical analysis, comparisons, and summaries of data, and presents analysis results through charts, graphs, and other visualizations, helping users more intuitively understand the multidimensional characteristics and inherent connections of complex datasets.

[0098] The customized report module generates customized reports according to user needs.

[0099] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A bird habitat early warning method based on climate change scenarios, characterized in that: The following steps are involved: S1: Based on meteorological data and greenhouse gas emission scenarios, construct different climate change scenarios and predict ecological and environmental changes under different scenarios; S2: Use species distribution models and habitat suitability models to simulate changes in bird habitat distribution under climate change scenarios; S3: Set early warning indicators based on the habitat characteristics and habitat changes of different bird species, including changes in habitat area and the degree of habitat fragmentation; S4: Build an integrated early warning system to generate risk warning information for bird habitats through real-time monitoring of climate and habitat data; S5: Display early warning information through visual means to provide intuitive risk levels and help relevant departments formulate response strategies.

2. The bird habitat early warning method based on climate change scenarios according to claim 1, characterized in that: S1 specifically includes: collecting and integrating meteorological data from global climate models and regional climate models, including temperature, humidity, precipitation, and wind; Using climate models to simulate climate change scenarios at different points in the future based on different greenhouse gas emission scenarios; Use ecological models to predict ecological and environmental changes under different climate change scenarios, including vegetation cover, soil moisture, and ecosystem productivity; S2 specifically includes: constructing bird species distribution models and habitat suitability models; Input climate change prediction results into species distribution model units and habitat suitability model units to simulate changes in bird habitat distribution under different climate change scenarios; Generate spatiotemporal distribution layers of bird habitats, including habitat area, habitat quality, and habitat physical distribution; S3 specifically includes: collecting and organizing ecological data on different bird species, including habitat preferences, food chain positions, and migration paths; According to the habitat characteristics of birds and simulation results, early warning indicators are set, including changes in habitat area, habitat fragmentation, and changes in habitat quality; Set a threshold for each warning indicator. When the simulation result exceeds the threshold, the warning is triggered. S4 specifically includes: building an early warning system framework, including a data acquisition module, a data processing module, a model calculation module, an early warning generation module, and an information push module; The data acquisition module collects meteorological data and habitat data in real time. The data processing module cleans and pre-processes the data. The model calculation module runs the species distribution model and habitat suitability model. The warning generation module generates risk warning information based on warning indicators. The information push module pushes the warning information to relevant departments and personnel through various channels. S5 specifically includes: building a visualization platform, including a geographic information system and visualization tools; Display warning information in various forms such as maps, charts, and reports, providing detailed information on risk level, risk area, and risk date; Provide customized visualization interfaces for users at different levels, supporting interactive query and analysis.

3. The bird habitat early warning method based on climate change scenarios according to claim 2, characterized in that: The model calculation module also includes an intelligent adaptive simulation optimization system, which includes a dynamic parameter adjustment module, a model adaptive learning module and a simulation result verification module, wherein: The dynamic parameter adjustment module adjusts model parameters according to real-time data to improve simulation accuracy; The model adaptive learning module uses machine learning algorithms to optimize the model structure and improve the generalization ability of the model; The simulation result verification module verifies the accuracy of the simulation results through the historical data verification unit and the measured data verification unit to ensure the reliability and effectiveness of the simulation results; The early warning generation module also includes a multi-factor comprehensive evaluation system, which includes an evaluation index library, a weight distribution module, and a comprehensive evaluation algorithm, among which: The evaluation index library contains multiple evaluation indicators related to ecological environment and bird habitats; The weight allocation module allocates the weight of each indicator based on expert knowledge and historical data; The comprehensive assessment algorithm relies on a multi-indicator comprehensive assessment model, through which a series of predefined indicators are quantitatively analyzed to calculate the final warning level.

4. The bird habitat early warning method based on climate change scenarios according to claim 2, characterized in that: The information push module also includes an intelligent push strategy system, which includes a user management module, a push rule library, and a push strategy optimization module, among which: The user management module maintains the personalized needs and preferences of different users; The push rule library contains various push rules and conditions; The push strategy optimization module optimizes the push strategy based on user feedback and push effects.

5. The bird habitat early warning method based on climate change scenarios according to claim 2, characterized in that: The visualization platform also includes an interactive query and analysis system, which includes a spatial query module, a temporal query module, a multidimensional analysis module, and a customized report module. The spatial query module supports query and analysis based on geographic location; The time query module supports query and analysis based on time range; The multidimensional analysis module supports multidimensional data analysis and visualization; The customized report module supports generating customized reports according to user needs.

6. The bird habitat early warning method based on climate change scenarios according to claim 2, characterized in that: The data collection module includes a meteorological data collection unit and a habitat data collection unit, wherein: The meteorological data acquisition unit collects meteorological data in real time through meteorological data acquisition equipment, satellite remote sensing data receiving equipment, ground monitoring stations and UAV monitoring equipment; The habitat data collection unit collects bird habitat data in real time through ground monitoring stations, drone monitoring equipment and other sensor equipment.

7. The bird habitat early warning method based on climate change scenarios according to claim 2, characterized in that: The data processing module includes a data cleaning unit and a data preprocessing unit, wherein: The data cleaning unit cleans the collected data; The data preprocessing unit performs standardization on the cleaned data.

8. The bird habitat early warning method based on climate change scenarios according to claim 1, characterized in that: The model calculation module includes the species distribution model unit and the habitat suitability model unit, where: The species distribution model unit constructs a bird species distribution model through the maximum entropy model MaxEnt and the niche model Niche; The habitat suitability model unit constructs a bird habitat suitability model through the ecological niche suitability model ECOMOD and the habitat suitability model HSM.

9. The bird habitat early warning method based on climate change scenarios according to claim 2, characterized in that: The early warning generation module includes an evaluation index library, a weight distribution module, and a comprehensive evaluation algorithm, among which: The evaluation index library contains multiple evaluation indicators related to ecological environment and bird habitats; The weight allocation module is responsible for assigning corresponding weights to each indicator in the evaluation system based on the preset weight allocation algorithm, combined with expert knowledge and historical data; The comprehensive assessment algorithm relies on a multi-indicator comprehensive assessment model, through which a series of predefined indicators are quantitatively analyzed to calculate the final warning level.

10. The bird habitat early warning method based on climate change scenarios according to claim 2, characterized in that: The information push module includes a user management module, a push rule library, and a push strategy optimization module, among which: The user management module maintains the personalized needs and preferences of different users; The push rule library contains various push rules and conditions; The push strategy optimization module is responsible for executing the push strategy optimization algorithm, which adjusts and improves the push strategy based on user feedback and push effect data.

Images