Ecological environment multi-source data intelligent collection and analysis system and method

Abstract

The present invention relates to the technical field of data intelligent collection and analysis systems, and more specifically, to an intelligent collection and analysis system for multi-source ecological environment data and a method thereof. The system comprises: a data collection module for acquiring multi-source ecological environment data; a data preprocessing module for cleaning and standardizing the multi-source ecological environment data; a data storage module for storing the preprocessed ecological environment data; a data analysis module for performing in-depth analysis on the stored ecological environment data; a result display module for visually presenting the analysis results; and a system management module for coordinating the operation of other modules. The data collection module, preprocessing module, storage module, analysis module, result display module and system management module form a complete closed loop. Each module can fully exert its function and work closely with other modules, which not only improves the operation efficiency of the system, but also enhances the stability and reliability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of data intelligent collection and analysis systems, and more specifically, to an ecological environment multi-source data intelligent collection and analysis system and method thereof. Background Art

[0002] In recent years, with the increasing prominence of environmental issues, ecological and environmental monitoring and analysis have become a global focus. Traditional ecological and environmental monitoring methods are often limited to a single data source, making it difficult to fully reflect the true state of complex ecosystems. While the development of multi-source data collection technology has provided a rich data foundation for ecological and environmental monitoring, effectively integrating and analyzing this heterogeneous data remains a significant challenge.

[0003] Currently, some research institutions and enterprises have developed various ecological and environmental monitoring systems that, to a certain extent, enable the collection and analysis of multi-source data. For example, some systems utilize technologies such as satellite remote sensing and ground-based sensor networks to achieve comprehensive monitoring of environmental factors such as the atmosphere, water bodies, and soil. Other systems incorporate machine learning algorithms to attempt to extract valuable information from massive amounts of data. However, these existing technologies still have many shortcomings.

[0004] First, most existing systems are still deficient in data fusion. They often simply overlay data from different sources without truly achieving deep integration and collaborative analysis. This leads to one-sided analytical results and makes it difficult to reveal the complex interactions within the ecosystem.

[0005] Second, existing analytical methods are mostly based on traditional statistical models or simple machine learning algorithms. These methods often struggle to process high-dimensional, nonlinear ecological data and are unable to capture the complex dynamics of ecosystems. Traditional methods are particularly unsatisfactory when dealing with the fractional-order dynamics common in ecosystems.

[0006] Furthermore, many existing systems fail to adequately consider the spatiotemporal characteristics of ecological data. Ecological and environmental data often exhibit strong spatiotemporal correlations, and ignoring this characteristic can lead to biased analytical results. While some systems have attempted to incorporate spatiotemporal analysis methods, most remain at the level of simple spatiotemporal interpolation or cluster analysis, failing to delve deeply into the essence of ecological processes.

[0007] Furthermore, existing systems have significant shortcomings in their predictive capabilities. Most systems can only provide short-term, localized forecasts, making it difficult to accurately predict long-term ecosystem trends. This significantly limits their application value in ecological and environmental management and decision support.

[0008] Finally, existing systems generally lack flexibility and scalability. Faced with ever-changing ecological and environmental issues and the emergence of new data sources, these systems often find it difficult to quickly adapt and upgrade, which severely limits their practicality and longevity.

[0009] Given the shortcomings of the aforementioned existing technologies, there is an urgent need for a new intelligent collection and analysis system for multi-source ecological and environmental data that can effectively address a series of key issues such as data fusion, complex system modeling, spatiotemporal characteristics analysis, and long-term trend prediction. The present invention is proposed to address these urgent needs. Summary of the Invention

[0010] The proposed system and method for intelligent collection and analysis of multi-source ecological and environmental data aims to address existing issues such as insufficient data integration, simplistic analysis methods, and limited predictive capabilities. Through innovative system architecture design and advanced algorithm application, this system achieves intelligent processing throughout the entire process, from data collection to in-depth analysis, significantly improving the accuracy and efficiency of ecological and environmental monitoring and analysis.

[0011] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0012] Ecological environment multi-source data intelligent collection and analysis system, the system includes:

[0013] Data acquisition module, used to obtain multi-source ecological environment data;

[0014] A data preprocessing module, used for cleaning and standardizing the multi-source ecological environment data;

[0015] A data storage module, used to store pre-processed ecological environment data;

[0016] A data analysis module, used to perform in-depth analysis on the stored ecological environment data;

[0017] Result display module, used to visualize the analysis results; and

[0018] System management module, used to coordinate the operation of other modules.

[0019] Preferably, the data analysis module includes:

[0020] A multidimensional ecological phase space construction unit for constructing ecosystem state representations based on multi-source data;

[0021] a topological feature extraction unit, configured to extract topological features from the ecosystem state representation; and

[0022] The nonlinear dynamics modeling unit is used to establish an ecosystem dynamics model based on the topological characteristics.

[0023] Preferably, the multidimensional ecological phase space construction unit obtains the phase space state vector by the following method: t =Φ(F t ,H t ,S t ), where X t ∈R d is the phase space state vector at time t, is the fused multi-source data feature vector, is the hidden state vector, is the system structure parameter vector, is a nonlinear mapping function, d is the phase space dimension, n f is the feature vector dimension, n h is the hidden state dimension, n s is the system structure parameter dimension.

[0024] Preferably, the topological feature extraction unit obtains the topological feature vector by: first, calculating the persistent homology: in, is a k-dimensional persistence graph, PH k is a k-dimensional persistent homology operator, is a simplicial complex constructed based on the phase space state in the time window w, K is the highest homology dimension considered; then, the topological eigenvector is extracted: Among them, T t is the topological eigenvector, and Ψ is the function that converts the persistence graph into the eigenvector.

[0025] Preferably, the nonlinear dynamics modeling unit establishes the dynamics model in the following manner: in, is a nonlinear dynamic function, θ is a model parameter, and the specific form is: Among them, A, B, W are weight matrices, c is the bias vector, g(t) is the time-dependent external driving function, E α,β is the Mittaq-Leffler function.

[0026] Preferably, the Mittag-Leffler function is defined as: Among them, Γ is the gamma function, α, β>0 are function parameters, and z is the function variable.

[0027] Preferably, the nonlinear dynamics modeling unit predicts the future state by: Where Δt is the prediction time step, τ is the integral variable, and the Runge-Kutta method with adaptive step size is used to solve the above integral.

[0028] The method for intelligent collection and analysis of multi-source ecological environment data based on the system includes the following steps:

[0029] Acquire multi-source ecological environment data through the data acquisition module;

[0030] Using the data preprocessing module to clean and standardize the acquired multi-source ecological environment data;

[0031] storing the preprocessed data in the data storage module;

[0032] Use the data analysis module to conduct in-depth analysis on the stored ecological environment data;

[0033] Visually present the analysis results through the result display module; and

[0034] The system management module coordinates the execution of the above steps.

[0035] Preferably, the analysis step of the data analysis module further includes:

[0036] Construct a multi-dimensional ecological phase space, integrating multi-source data and system structure information;

[0037] Extract topological features based on the constructed phase space to capture the geometric and topological properties of the system; and

[0038] Topological features are used to construct nonlinear dynamic models to predict ecosystem status.

[0039] Preferably, the step of constructing the nonlinear dynamic model further comprises:

[0040] The Mittag-Leffler function is selected as the activation function to enhance the model's ability to express fractional-order dynamics;

[0041] Construct dynamic equations by comprehensively considering the system state, topological characteristics, and time dependence; and

[0042] The dynamic equations are solved using a numerical integration method with an adaptive step size to obtain a prediction of the future state of the system.

[0043] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0044] First, from a holistic perspective, the system proposed in this paper achieves the organic integration and coordinated operation of multiple functional modules. The data acquisition module, preprocessing module, storage module, analysis module, result display module, and system management module form a complete closed loop, allowing each module to fully utilize its functions while cooperating closely with the others. This integrated design not only improves the system's operational efficiency but also enhances its stability and reliability.

[0045] In terms of data processing, a major innovation of this invention lies in the deep integration of heterogeneous data from multiple sources. By constructing a multidimensional ecological phase space, the system can unify data from different sensors and scales into a single high-dimensional space for analysis. This approach effectively solves the data fragmentation problem in traditional systems and lays a solid foundation for subsequent in-depth analysis.

[0046] In terms of analytical methods, this invention introduces advanced technologies such as topological data analysis and nonlinear dynamic modeling. The topological feature extraction unit can capture the geometric and topological information contained in the data, which often reflects the essential structure and relationships in the ecosystem. The nonlinear dynamic modeling unit greatly enhances the model's ability to express complex ecological processes by introducing the Mittag-Leffler function as an activation function. The combination of these two technologies enables the system to deeply reveal the complex interactions in the ecosystem, providing a more reliable scientific basis for ecological and environmental management.

[0047] In terms of predictive capabilities, the system of this invention constructs a highly accurate dynamic equation by comprehensively considering system state, topological characteristics, and time dependence. Combined with a numerical integration method with adaptive step size, the system can not only accurately predict short-term changes but also reliably forecast long-term ecosystem evolution trends. This greatly enhances the system's application value in ecological and environmental planning and decision support.

[0048] Furthermore, the system of this invention exhibits excellent adaptability and scalability. Through its modular design and advanced algorithmic framework, the system can easily adapt to the integration of new data sources and new analytical requirements. This means that the system can be continuously upgraded and optimized, maintaining its advanced nature and practicality over the long term.

[0049] Finally, the system has proven itself to be highly effective across a wide range of ecological and environmental monitoring scenarios. Whether used in forest ecosystem monitoring, wetland protection, or urban air quality management, it provides accurate and timely analysis and early warning information. This broad applicability makes it a powerful tool for ecological and environmental protection efforts.

[0050] In summary, this invention effectively addresses numerous issues existing in existing ecological and environmental monitoring systems through innovative system design and advanced algorithmic application. It not only improves the accuracy and efficiency of ecological and environmental data analysis but also provides strong support for ecological and environmental protection and management decision-making. As environmental issues become increasingly complex, the value of this invention will become increasingly prominent, and it is expected to play a significant role in global ecological and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flowchart of the overall system of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the scheme in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all the embodiments. The embodiments of the present invention and all other embodiments obtained by ordinary persons in this field without making creative work are within the scope of protection of the present invention.

[0053] like Figure 1 As shown in FIG, the intelligent ecological environment multi-source data collection and analysis system of the present invention includes a data collection module 1, a data preprocessing module 2, a data storage module 3, a data analysis module 4, a result display module 5, and a system management module 6. These modules work together to form a complete data processing flow.

[0054] Data acquisition module 1 is responsible for acquiring multi-source ecological and environmental data. In one embodiment of the present invention, this module may include various sensors distributed across different ecological environments, such as air quality sensors, water quality sensors, and soil sensors. These sensors can collect a variety of environmental parameters such as temperature, humidity, pH, and pollutant concentrations in real time. Preferably, the frequency of data acquisition can be adjusted based on the changing characteristics of different environmental parameters. For example, air quality data may need to be collected every hour, while soil data may only need to be collected once a day.

[0055] Data preprocessing module 2 cleans and standardizes the collected multi-source ecological and environmental data. This step is crucial because the raw data may contain noise, outliers, or missing values. The present invention employs an adaptive data cleaning algorithm that automatically adjusts cleaning parameters based on the characteristics of different data sources. For example, for temperature data, a reasonable range of variation can be set, such as -50°C to 50°C. Data outside this range will be marked as abnormal and processed.

[0056] Data storage module 3 is used to store preprocessed ecological and environmental data. Considering the diversity and large scale of ecological and environmental data, the present invention utilizes a distributed storage system to effectively process large amounts of heterogeneous data. Preferably, data is stored in a time series format to facilitate subsequent time series analysis.

[0057] The data analysis module 4 is the core of the present invention and is used to perform in-depth analysis on the stored ecological environment data.

[0058] In a preferred embodiment of the present invention, the module includes a multidimensional ecological phase space construction unit 41, a topological feature extraction unit 42, and a nonlinear dynamics modeling unit 43. The coordinated operation of these three units enables the system to deeply understand the dynamic changes of complex ecosystems.

[0059] In one embodiment of the present invention, the multi-dimensional ecological phase space construction unit 41 obtains the phase space state vector by:

[0060] X t =Φ(F t ,H t ,S t )

[0061] Among them, X t ∈R d is the phase space state vector at time t, is the fused multi-source data feature vector, is the hidden state vector, is the system structure parameter vector, is a nonlinear mapping function.

[0062] In practical applications, the choice of d depends on the complexity of the ecosystem and can usually be selected between 50 and 200. f Depending on the number of environmental parameters collected, it may be between 10 and 50. h and n s The choice of n needs to be determined through experiments, usually n h You can choose n f 1.5 to 2 times of n s You can choose n f 0.5 to 1 times.

[0063] In one embodiment of the present invention, the topological feature extraction unit 42 first calculates the persistent coherence:

[0064]

[0065] in, is a k-dimensional persistence graph, PH kis a k-dimensional persistent homology operator, is a simplicial complex constructed based on the phase space state in the time window w. Then, the topological eigenvector is extracted:

[0066]

[0067] Among them, T t is the topological eigenvector, and Ψ is the function that converts the persistence graph into an eigenvector. In ecological and environmental analysis, the choice of the time window w is crucial. For rapidly changing parameters (such as air quality), w can be 24 hours; for slowly changing parameters (such as soil quality), w can be a week or longer. K is typically chosen not to exceed 3, as high-dimensional homology information is of little significance in ecosystems.

[0068] In one embodiment of the present invention, the nonlinear dynamics modeling unit 43 establishes the dynamics model in the following manner:

[0069] The nonlinear dynamics modeling unit 43 establishes the dynamics model in the following manner:

[0070]

[0071] in, is a nonlinear dynamic function, and θ is a model parameter. The specific form is:

[0072]

[0073] Among them, A, B, W are weight matrices, c is the bias vector, g(t) is the time-dependent external driving function, E α,β is the Mittag-Leffler function.

[0074] This nonlinear dynamic model can capture the complex interactions within ecosystems. For example, in wetland ecosystems, the complex nonlinear relationship between water level, vegetation cover, and bird populations is difficult to accurately describe with traditional linear models. By introducing the Mittag-Leffler function as an activation function, the model of this invention enhances its ability to express this complex relationship.

[0075] Preferably, the values of α and β can be determined through cross-validation, usually α is between 0.5 and 1, and β is between 0.1 and 0.5. This choice can avoid overfitting while maintaining the expressiveness of the model.

[0076] The results display module 5 is responsible for visually presenting the analysis results. The present invention uses interactive visualization technology to enable ecologists and environmental managers to intuitively understand complex ecosystem dynamics. For example, 3D graphics can be used to display the interactions between different environmental factors, or time series graphs can be used to display long-term ecosystem trends.

[0077] The system management module 6 coordinates the operation of other modules to ensure the efficient operation of the entire system. It is also responsible for functions such as user rights management and system log recording to ensure the security and traceability of the system.

[0078] The present invention's intelligent ecological multi-source data collection and analysis system, through the collaborative work of the aforementioned modules, achieves intelligent processing throughout the entire process, from data collection to in-depth analysis. Compared to traditional methods, this system offers the following advantages: First, the fusion of multi-source data and in-depth analysis improve the comprehensiveness and accuracy of ecological and environmental assessments; second, topological feature extraction captures complex structural relationships within ecosystems; and finally, nonlinear dynamic modeling enhances the ability to predict the future state of ecosystems.

[0079] In practical applications, this system can be used in a variety of ecological and environmental monitoring and early warning scenarios. For example, in forest ecosystem monitoring, it can comprehensively analyze multiple factors such as temperature, precipitation, soil moisture, and vegetation coverage to predict forest fire risks or the likelihood of pest and disease outbreaks. In wetland ecosystem protection, it can analyze parameters such as water level changes, pollutant concentrations, and biodiversity indices to assess wetland health and provide protection recommendations.

[0080] In summary, the intelligent collection and analysis system and method of multi-source ecological environmental data provided by the present invention provide strong technical support for ecological environmental protection and management through innovative data processing and analysis technologies, and are expected to play an important role in addressing global ecological environmental challenges.

[0081] In one embodiment of the present invention, the Mittag-Leffler function used in the present invention is defined as:

[0082]

[0083] Where Γ is the gamma function, α, β>0 are function parameters, and z is the function variable. The Mittag-Leffler function is an important special function with extensive applications in fractional calculus and complex system modeling. The Mittag-Leffler function is introduced as an activation function in this paper primarily because ecosystems often exhibit fractional-order dynamics.

[0084] For example, when studying the carbon cycle in forest ecosystems, tree growth and carbon uptake do not follow simple integer-order differential equations but instead exhibit fractional-order characteristics. By using the Mittag-Leffler function, our model can more accurately capture this complex dynamic process. In practical applications, the selection of α and β needs to be adjusted based on the specific characteristics of the ecosystem. Preferably, the optimal values of α and β can be found on the training data using methods such as grid search.

[0085] It is worth noting that the calculation of the Mittag-Leffler function may be more complex than traditional activation functions (such as ReLU or sigmoid). To improve computational efficiency, the present invention adopts an approximate calculation method based on the fast Fourier transform, which greatly reduces the computational complexity and makes the function applicable in real-time systems.

[0086] In one embodiment of the present invention, the nonlinear dynamics modeling unit 43 predicts the future state by:

[0087]

[0088] Where Δt is the prediction time step, and τ is the integral variable. This integral equation is essentially an initial value problem, describing the time evolution of the system state. In practice, we use the Runge-Kutta method with an adaptive step size to solve this integral.

[0089] The selection of an adaptive step size is crucial for improving computational efficiency and ensuring numerical stability. In a preferred embodiment of the present invention, we use the Dormand-Prince method (also known as the RKDP method) as a specific implementation of the adaptive step size Runge-Kutta method. This method automatically adjusts the step size based on the local truncation error, improving computational efficiency while maintaining accuracy.

[0090] For example, when predicting wetland water level changes, the algorithm automatically increases the step size to reduce the computational effort when the water level changes relatively slowly. However, when the water level changes dramatically, such as during a rainstorm, the algorithm automatically decreases the step size to capture rapidly changing dynamics. This adaptive nature enables the system to efficiently handle ecological processes across a wide range of timescales.

[0091] In a preferred embodiment of the present invention, a method for intelligently collecting and analyzing multi-source ecological and environmental data based on the above-mentioned system is also provided. The method comprises the following steps: acquiring multi-source ecological and environmental data through a data acquisition module 1; cleaning and standardizing the acquired multi-source ecological and environmental data through a data preprocessing module 2; storing the preprocessed data in a data storage module 3; performing in-depth analysis of the stored ecological and environmental data through a data analysis module 4; visually presenting the analysis results through a result display module 5; and coordinating the execution of the above steps through a system management module 6.

[0092] A key feature of this approach is its iterative nature. In practice, ecological and environmental data is continuously generated, requiring the system to continuously collect, process, and analyze data. Therefore, the aforementioned steps form a closed loop, allowing the system to continuously operate, constantly updating and optimizing the analysis results.

[0093] For example, when monitoring urban air quality, the system continuously collects air quality data from various monitoring points and updates the analysis results in real time. If it detects an abnormally high concentration of pollutants in a particular area, the system immediately issues an alert and initiates a more sophisticated analysis process, such as increasing the data sampling frequency and invoking more complex prediction models.

[0094] In a preferred embodiment of the present invention, the analysis steps of the data analysis module 4 further include: constructing a multidimensional ecological phase space to integrate multi-source data and system structure information; extracting topological features based on the constructed phase space to capture the geometric and topological properties of the system; and using topological features to construct a nonlinear dynamic model to achieve ecosystem state prediction.

[0095] These three steps constitute the main flow of the core algorithm of the present invention. It is worth noting that there is a close connection and feedback mechanism between these three steps. For example, the results of topological feature extraction will in turn affect the construction of the phase space, and the prediction results of the dynamic model may also lead to a reassessment of the phase space structure.

[0096] In practical applications, this feedback mechanism enables the system to continuously optimize itself. For example, when monitoring a forest ecosystem, if the model's predicted tree growth rate differs significantly from the actual observed value, the system will automatically adjust the way it constructs the phase space, perhaps adding new dimensions (such as introducing indicators of soil microbial activity) or adjusting the weights of existing dimensions.

[0097] In a preferred embodiment of the present invention, the steps of constructing the nonlinear dynamic model further include: selecting the Mittag-Leffler function as the activation function to enhance the model's ability to express fractional-order dynamics; constructing the dynamic equation by comprehensively considering the system state, topological characteristics and time dependence; and using the numerical integration method with adaptive step size to solve the dynamic equation to obtain a prediction of the future state of the system.

[0098] This step is the core of the entire analysis process and the most innovative part of the present invention. By introducing the Mittag-Leffler function and topological features, the model of the present invention is able to capture complex ecological processes that are difficult to describe with traditional methods. For example, when studying marine ecosystems, traditional models often struggle to accurately describe the clustering behavior of fish schools and their impact on the ecosystem. However, by introducing topological features, the model of the present invention can effectively capture the spatial structure of fish schools, thereby more accurately predicting their impact on the ecosystem.

[0099] Furthermore, the adaptive step-size numerical integration method not only improves computational efficiency but also enhances the model's adaptability to ecological processes at different time scales. For example, within the same model, we can simultaneously predict rapidly changing weather factors (such as rainfall) and slowly changing ecological factors (such as vegetation cover), without having to construct separate models for different time scales.

[0100] In summary, the intelligent ecological multi-source data collection and analysis system and method provided by this invention, through innovative algorithm design and system architecture, achieves efficient and accurate analysis and prediction of complex ecosystems. This system can be applied not only to environmental monitoring and ecological protection, but also to provide important decision-making support in areas such as urban planning and agricultural production. With the further development of Internet of Things technology and artificial intelligence algorithms, this system has significant room for optimization and expansion, and is expected to play an even more important role in addressing global ecological and environmental challenges.

[0101] The above description is only a preferred specific embodiment of the present invention; however, the protection scope of the present invention is not limited thereto; any person familiar with the art who, within the scope disclosed by the present invention, makes substitutions or changes based on the scheme of the present invention and its improved concepts shall be covered within the protection scope of the present invention.

Claims

1. Ecological environment multi-source data intelligent collection and analysis system, characterized by: The system includes: Data acquisition module, used to obtain multi-source ecological environment data; A data preprocessing module, used for cleaning and standardizing the multi-source ecological environment data; A data storage module, used to store pre-processed ecological environment data; A data analysis module, used to perform in-depth analysis on the stored ecological environment data; Result display module, used to visualize the analysis results; and System management module, used to coordinate the operation of other modules; The data analysis module includes: A multidimensional ecological phase space construction unit for constructing ecosystem state representations based on multi-source data; a topological feature extraction unit, configured to extract topological features from the ecosystem state representation; and a nonlinear dynamics modeling unit, configured to establish an ecosystem dynamics model based on the topological features; The multidimensional ecological phase space construction unit obtains the phase space state vector by the following method: t =Φ(F t ,H t ,S t ), where X t ∈R d is the phase space state vector at time t, is the fused multi-source data feature vector, is the hidden state vector, is the system structure parameter vector, is a nonlinear mapping function, d is the phase space dimension, n f is the feature vector dimension, n h is the hidden state dimension, n s is the system structure parameter dimension; The topological feature extraction unit obtains the topological feature vector by: first, calculating the persistent homology: in, is a k-dimensional persistence graph, PH k is a k-dimensional persistent homology operator, is a simplicial complex constructed based on the phase space state in the time window w, K is the highest homology dimension considered; then, the topological eigenvector is extracted: Among them, T t is the topological eigenvector, Ψ is the function that transforms the persistence graph into the eigenvector; The nonlinear dynamics modeling unit establishes the dynamics model in the following manner: in, is a nonlinear dynamic function, θ is a model parameter, and the specific form is: Among them, A, B, W are weight matrices, c is the bias vector, g(t) is the time-dependent external driving function, E α,β is the Mittaq-Leffler function; The Mittag-Leffler function is defined as: Among them, Γ is the gamma function, α, β>0 are function parameters, and z is the function variable.

2. The system according to claim 1, wherein: The nonlinear dynamics modeling unit predicts future states by: Where Δt is the prediction time step, τ is the integral variable, and the Runge-Kutta method with adaptive step size is used to solve the above integral.

3. The method for intelligent collection and analysis of multi-source ecological environment data based on the system according to any one of claims 1-2 is characterized in that: The method comprises the following steps: Acquire multi-source ecological environment data through the data acquisition module; Using the data preprocessing module to clean and standardize the acquired multi-source ecological environment data; storing the preprocessed data in the data storage module; Use the data analysis module to conduct in-depth analysis on the stored ecological environment data; Visually present the analysis results through the result display module; and The system management module coordinates the execution of the above steps.

4. The method according to claim 3, characterized in that The analysis step of the data analysis module further includes: Construct a multi-dimensional ecological phase space, integrating multi-source data and system structure information; Extract topological features based on the constructed phase space to capture the geometric and topological properties of the system; and Topological features are used to construct nonlinear dynamic models to predict ecosystem status.

5. The method according to claim 4, characterized in that The step of constructing the nonlinear dynamic model further includes: The Mittag-Leffler function is selected as the activation function to enhance the model's ability to express fractional-order dynamics; The dynamic equations are constructed by comprehensively considering the system state, topological characteristics and time dependence; and the dynamic equations are solved using the numerical integration method with adaptive step size to obtain the prediction of the future state of the system.

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