Ecological environment multi-source data intelligent acquisition and analysis system and method thereof

Abstract

The invention relates to the technical field of data intelligent acquisition and analysis systems, in particular to an ecological environment multi-source data intelligent acquisition and analysis system and method, and the system comprises a data acquisition module which is used for obtaining multi-source ecological environment data; the data preprocessing module is used for cleaning and standardizing the multi-source ecological environment data; the data storage module is used for storing the preprocessed ecological environment data; the data analysis module is used for carrying out deep analysis on the stored ecological environment data; the result display module is used for visually displaying the analysis result; the system management module is used for coordinating the operation of other modules, the data acquisition module, the preprocessing module, the storage module, the analysis module, the result display module and the system management module form a complete closed loop, each module can give full play to the function and is tightly matched with other modules at the same time, the operation efficiency of the system is improved, and the service life of the system is prolonged. And the stability and the reliability of the system are enhanced.

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, as environmental problems have become increasingly prominent, ecological environment monitoring and analysis have become the focus of global attention. Traditional ecological environment monitoring methods are often limited to a single data source and are difficult to fully reflect the true status of complex ecosystems. Although the development of multi-source data acquisition technology has provided a rich data foundation for ecological environment monitoring, how to effectively integrate and analyze these heterogeneous data remains a huge challenge.

[0003] At present, some research institutions and enterprises have developed various ecological environment monitoring systems, which have achieved the collection and analysis of multi-source data to a certain extent. For example, some systems use satellite remote sensing, ground sensor networks and other technologies to achieve comprehensive monitoring of environmental elements such as atmosphere, water, and soil. Some systems also introduce machine learning algorithms to try to mine valuable information from massive data. However, these existing technologies still have many shortcomings.

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

[0005] Secondly, most existing analysis methods are based on traditional statistical models or simple machine learning algorithms. These methods are often unable to cope with high-dimensional, nonlinear ecological data and have difficulty capturing the complex dynamic processes in ecosystems. In particular, when faced with the fractional-order dynamic characteristics commonly seen in ecosystems, the performance of traditional methods is even more unsatisfactory.

[0006] Furthermore, many existing systems lack sufficient consideration of the spatiotemporal characteristics of ecological data. Ecological and environmental data usually have strong spatiotemporal correlations, and ignoring this characteristic will lead to biased analysis results. Although some systems have tried to introduce spatiotemporal analysis methods, most of them remain at the level of simple spatiotemporal interpolation or cluster analysis, which makes it difficult to deeply reveal the essence of ecological processes.

[0007] In addition, existing systems also have obvious deficiencies in predictive capabilities. Most systems can only provide short-term, local predictions and are unable to accurately predict the long-term evolution trend of ecosystems. This greatly limits the application value of these systems in ecological environment management and decision support.

[0008] Finally, existing systems generally lack flexibility and scalability. Faced with ever-changing ecological and environmental issues and emerging new data sources, these systems are often difficult to adapt and upgrade quickly, resulting in serious limitations on their practicality and lifespan.

[0009] In view of the above shortcomings of the prior art, there is an urgent need for a new type of ecological environment multi-source data intelligent collection and analysis system that can effectively solve a series of key problems 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 ecological environment multi-source data intelligent collection and analysis system and method proposed in this invention are intended to solve the problems of insufficient data fusion, simple analysis methods, and limited prediction capabilities in the prior art. Through innovative system architecture design and advanced algorithm application, this invention realizes intelligent processing of the entire process from data collection to in-depth analysis, greatly improving the accuracy and efficiency of ecological environment 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 collection 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 for storing pre-processed ecological environment data;

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

[0017] Results 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 comprises:

[0020] A multidimensional ecological phase space construction unit for constructing ecosystem state representation 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 the 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 It is the system structure parameter dimension.

[0024] Preferably, the topological feature extraction unit obtains the topological feature vector by: first, calculating the continuous homology: in, is a k-dimensional persistence graph, PH k is a k-dimensional continuous 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 transforms 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: Among them, 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 comprises 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] Using 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 comprises:

[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] The topological features are used to construct nonlinear dynamic models to predict the state of the ecosystem.

[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] Constructing dynamic equations by taking into account 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 the perspective of the overall architecture, the system proposed in the present invention realizes the organic combination and coordinated work 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, and each module can give full play to its function while cooperating closely with other modules. This overall design not only improves the operating efficiency of the system, but also enhances the stability and reliability of the system.

[0045] In terms of data processing, a major innovation of the present invention is the realization of deep fusion of multi-source heterogeneous data. Through the construction of a multi-dimensional ecological phase space, the system can unify data from different sensors and different scales into a high-dimensional space for analysis. This method effectively solves the problem of data fragmentation in traditional systems and lays a solid foundation for subsequent in-depth analysis.

[0046] In terms of analysis methods, the present 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 relationship 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 and provide a more reliable scientific basis for ecological environmental management.

[0047] In terms of predictive capability, the system of the present invention constructs a highly accurate dynamic equation by comprehensively considering the system state, topological characteristics and time dependence. With the numerical integration method of adaptive step size, the system can not only accurately predict short-term changes, but also make reliable predictions on the long-term evolution trend of the ecosystem. This greatly enhances the application value of the system in ecological environment planning and decision support.

[0048] In addition, the system of the present invention also exhibits excellent adaptability and scalability. Through modular design and advanced algorithm framework, the system can easily cope with the access of new data sources and new analysis requirements. This means that the system can be continuously upgraded and optimized to maintain its advancement and practicality for a long time.

[0049] Finally, from the application effect point of view, the system of the present invention performs well in multiple ecological environment monitoring scenarios. Whether in forest ecosystem monitoring, wetland protection, or urban air quality management, the system can provide accurate and timely analysis results and early warning information. This wide applicability makes this system a powerful tool for ecological environment protection work.

[0050] In general, the present invention effectively solves many problems existing in the existing ecological environment monitoring system through innovative system design and advanced algorithm application. It not only improves the accuracy and efficiency of ecological environment data analysis, but also provides strong support for ecological environment protection and management decision-making. As environmental problems become increasingly complex, the value of this invention will become more and more prominent, and it is expected to play an important role in the global ecological environment protection cause. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0052] The scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. The embodiments in the present invention and all other embodiments obtained by ordinary persons in the art without making creative work are within the scope of protection of the present invention.

[0053] like Figure 1 As shown, the ecological environment multi-source data intelligent 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] The data acquisition module 1 is responsible for acquiring multi-source ecological environment data. In one embodiment of the present invention, the module may include various sensors distributed in different ecological environments, such as air quality sensors, water quality sensors, soil sensors, etc. These sensors can collect various environmental parameters such as temperature, humidity, pH value, pollutant concentration, etc. in real time. Preferably, the frequency of data acquisition can be adjusted according to the changing characteristics of different environmental parameters. For example, air quality data may need to be collected once an hour, while soil data may only need to be collected once a day.

[0055] The data preprocessing module 2 cleans and standardizes the collected multi-source ecological environment data. This step is crucial because the original data may contain noise, outliers or missing values. The present invention adopts an adaptive data cleaning algorithm that can automatically adjust the cleaning parameters according to 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, and data outside this range will be marked as abnormal and processed.

[0056] The data storage module 3 is used to store preprocessed ecological environment data. Considering the diversity and large-scale nature of ecological environment data, the present invention adopts a distributed storage system, which can effectively process large-scale heterogeneous data. Preferably, the data is stored in the form of time series 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 the preferred embodiment of the present invention, the module includes a multi-dimensional ecological phase space construction unit 41, a topological feature extraction unit 42, and a nonlinear dynamics modeling unit 43. The coordinated work 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 multidimensional ecological phase space construction unit 41 obtains the phase space state vector in the following manner:

[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 chosen 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 continuous 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 very important. For fast-changing parameters (such as air quality), w can be selected as 24 hours; for slowly changing parameters (such as soil quality), w can be selected as a week or longer. The choice of K is usually not more than 3, because high-dimensional homology information is of little significance in the ecosystem.

[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 in the ecosystem. For example, in a wetland ecosystem, there is a complex nonlinear relationship between water level, vegetation coverage and bird population, which is difficult to accurately describe with traditional linear models. The model of the present invention enhances the ability to express this complex relationship by introducing the Mittag-Leffler function as an activation function.

[0075] Preferably, the values ​​of α and β can be determined by 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 result display module 5 is responsible for visualizing the analysis results. The present invention uses interactive visualization technology to enable ecologists and environmental managers to intuitively understand the complex ecosystem dynamics. For example, the interaction between different environmental factors can be displayed through 3D graphics, or the long-term change trend of the ecosystem can be displayed through a time series graph.

[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 authority management and system log recording to ensure the security and traceability of the system.

[0078] The ecological environment multi-source data intelligent collection and analysis system of the present invention realizes the intelligent processing of the whole process from data collection to in-depth analysis through the collaborative work of the above modules. Compared with traditional methods, this system has the following advantages: first, the fusion and in-depth analysis of multi-source data improve the comprehensiveness and accuracy of ecological environment assessment; second, topological feature extraction can capture the complex structural relationship in the ecosystem; finally, nonlinear dynamic modeling improves the ability to predict the future state of the ecosystem.

[0079] In practical applications, this system can be used in a variety of ecological environment monitoring and early warning scenarios. For example, in forest ecosystem monitoring, it can comprehensively analyze multiple factors such as temperature, precipitation, soil moisture, vegetation coverage, etc. to predict the risk of forest fires or the possibility of pests and diseases. In wetland ecosystem protection, it can evaluate the health of wetlands and make protection recommendations by analyzing parameters such as water level changes, pollutant concentrations, and biodiversity indexes.

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

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

[0082]

[0083] Wherein, 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, which is widely used in fractional calculus and complex system modeling. The Mittag-Leffler function is introduced as an activation function in the present invention, mainly considering that the ecosystem often exhibits fractional-order dynamic characteristics.

[0084] For example, when studying the carbon cycle of forest ecosystems, the growth and carbon absorption of trees do not follow simple integer-order differential equations, but rather 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 according to the specific characteristics of the ecosystem. Preferably, the optimal α and β values ​​can be found on the training data through methods such as grid search.

[0085] It is worth noting that the calculation of the Mittag-Leffler function may be more complicated than traditional activation functions (such as ReLU or sigmoid). In order to improve the calculation efficiency, the present invention adopts an approximate calculation method based on fast Fourier transform, which greatly reduces the calculation complexity and enables the function to be applied 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] Among them, Δt is the prediction time step and τ is the integral variable. This integral equation is essentially an initial value problem, which describes the evolution of the system state over time. In practical applications, we use the Runge-Kutta method with adaptive step size to solve the above integral.

[0089] The selection of adaptive step size is crucial to 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 can automatically adjust the step size according to the local truncation error, improving computational efficiency while ensuring accuracy.

[0090] For example, when predicting wetland water level changes, when the water level changes relatively slowly, the algorithm will automatically increase the step size to reduce the amount of calculation; while in the case of heavy rains and other conditions that cause rapid changes in water levels, the algorithm will automatically reduce the step size to capture the rapidly changing dynamic process. This adaptive feature enables the system to efficiently handle ecological processes of various time scales.

[0091] In a preferred embodiment of the present invention, the present invention also provides an intelligent collection and analysis method of multi-source ecological environment data based on the above system. The method includes the following steps: acquiring multi-source ecological environment data through the data acquisition module 1; cleaning and standardizing the acquired multi-source ecological environment data using the data preprocessing module 2; storing the preprocessed data in the data storage module 3; performing in-depth analysis on the stored ecological environment data using the data analysis module 4; visually presenting the analysis results through the result display module 5; and coordinating the execution of the above steps by the system management module 6.

[0092] An important feature of this approach is its iterative nature. In practical applications, ecological and environmental data are continuously generated, and the system needs to continuously collect, process and analyze data. Therefore, the above steps form a closed loop, and the system can continue to run, constantly updating and optimizing the analysis results.

[0093] For example, when monitoring urban air quality, the system will continuously collect air quality data from each monitoring point and update the analysis results in real time. If it detects an abnormal increase in pollutant concentration in a certain area, the system will immediately issue an alarm and initiate a more sophisticated analysis process, such as increasing the data sampling frequency and calling a more complex prediction model.

[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 process 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 phase space, and the prediction results of the dynamic model may also lead to a re-evaluation 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 predicts a significant difference in tree growth rate from the actual observed value, the system will automatically adjust the way the phase space is constructed, which may add new dimensions (such as introducing indicators of soil microbial activity) or adjust 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; comprehensively considering the system state, topological characteristics and time dependence to construct the dynamic equation; and using the numerical integration method with an 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 can capture complex ecological processes that are difficult to describe with traditional methods. For example, when studying marine ecosystems, traditional models often have difficulty accurately describing 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 structural characteristics of fish schools, thereby more accurately predicting their impact on the ecosystem.

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

[0100] In general, the ecological environment multi-source data intelligent collection and analysis system and method provided by the present invention realizes efficient and accurate analysis and prediction of complex ecosystems through innovative algorithm design and system architecture. The system can not only be applied to environmental monitoring and ecological protection, but also provide important decision-making support for urban planning, agricultural production and other fields. In the future, with the further development of Internet of Things technology and artificial intelligence algorithms, this system still has a lot of room for optimization and expansion, and is expected to play a more important role in responding to global ecological and environmental challenges.

[0101] The above description is only a preferred specific implementation manner of the present invention, but 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 and improved concepts of the present invention 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 collection 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 for storing pre-processed ecological environment data; A data analysis module, used for performing in-depth analysis on the stored ecological environment data; Results display module, used to visualize the analysis results; and System management module, used to coordinate the operation of other modules.

2. The system according to claim 1, characterized in that The data analysis module includes: A multidimensional ecological phase space construction unit for constructing ecosystem state representation based on multi-source data; a topological feature extraction unit, configured to extract topological features from the ecosystem state representation; and The nonlinear dynamics modeling unit is used to establish an ecosystem dynamics model based on the topological characteristics.

3. The system according to claim 2, characterized in that The multidimensional ecological phase space construction unit obtains the phase space state vector in the following way: 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 the 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 It is the system structure parameter dimension.

4. The system according to claim 3, characterized in that The topological feature extraction unit obtains the topological feature vector by: first, calculating the continuous homology: in, is a k-dimensional persistence graph, PH k is a k-dimensional continuous 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 transforms the persistence graph into the eigenvector.

5. The system according to claim 4, characterized in that 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, is the time-dependent external driving function, E α,β is the Mittaq-Leffler function.

6. The system according to claim 5, characterized in that 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.

7. The system according to claim 6, characterized in that The nonlinear dynamics modeling unit predicts future states by: Among them, 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.

8. A method for intelligently collecting and analyzing multi-source ecological environment data based on the system according to any one of claims 1 to 7, 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; Using 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.

9. The method according to claim 8, 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 The topological features are used to construct nonlinear dynamic models to predict the state of the ecosystem.

10. The method according to claim 9, characterized in that The step of constructing the nonlinear dynamic model further comprises: The Mittag-Leffler function is selected as the activation function to enhance the model's ability to express fractional-order dynamics; Constructing dynamic equations by taking into account the system state, topological characteristics, and time dependence; and 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.

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