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CN119537810B - Ecological environment multi-source data intelligent acquisition and analysis system and method thereof - Google Patents

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

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Publication number
CN119537810B
CN119537810B CN202411685701.3A CN202411685701A CN119537810B CN 119537810 B CN119537810 B CN 119537810B CN 202411685701 A CN202411685701 A CN 202411685701A CN 119537810 B CN119537810 B CN 119537810B
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CN119537810A (en
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阎伟
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Qingdao Ecological Environment Monitoring Center Of Shandong Province Environmental Monitoring Sub Station Of China Environmental Monitoring Station Near Yellow Sea
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Qingdao Ecological Environment Monitoring Center Of Shandong Province Environmental Monitoring Sub Station Of China Environmental Monitoring Station Near Yellow Sea
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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 a method thereof, wherein the system comprises a data acquisition module, a data analysis module and a data analysis module, wherein the data acquisition module is used for acquiring multi-source ecological environment data; the system comprises a data preprocessing module, a data storage module, a data analysis module, a result display module and a system management module, wherein the data preprocessing module is used for cleaning and standardizing multisource ecological environment data, the data storage module is used for storing 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 analysis results, 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 fully play the functions of the modules and is tightly matched with the other modules, so that the operation efficiency of the system is improved, and the stability and the reliability of the system are enhanced.

Description

Ecological environment multi-source data intelligent acquisition and analysis system and method thereof
Technical Field
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 a method thereof.
Background
In recent years, with increasingly prominent environmental problems, ecological environmental monitoring and analysis have become a global focus of attention. Traditional ecological environment monitoring methods are often limited to a single data source, and the real condition of a complex ecological system is difficult to comprehensively reflect. While the development of multi-source data acquisition technology provides a rich data base for ecological environmental monitoring, how to efficiently integrate and analyze such heterogeneous data remains a significant challenge.
At present, various ecological environment monitoring systems have been developed by some research institutions and enterprises, and the systems realize multi-source data acquisition and analysis to a certain extent. For example, some systems utilize technologies such as satellite remote sensing, ground sensor networks, etc., to realize comprehensive monitoring of environmental elements such as atmosphere, water, soil, etc. Still other systems incorporate machine learning algorithms that attempt to mine valuable information from massive amounts of data. However, these prior art techniques still suffer from a number of deficiencies.
First, most existing systems remain deficient in data fusion. They tend to simply overlay data from different sources without actually achieving deep fusion and collaborative analysis between the data. This results in one-sided nature of the analysis results, making it difficult to reveal complex interactions in the ecosystem.
Second, existing analysis methods are mostly based on traditional statistical models or simple machine learning algorithms. These methods tend to be frustrating in processing high-dimensional, non-linear ecological data and difficult to capture complex dynamic processes in the ecosystem. The traditional approach performs even more poorly, especially in the face of fractional dynamics common in ecosystems.
Furthermore, many existing systems lack sufficient consideration for the spatiotemporal nature of ecological data. Ecological data often has a strong spatiotemporal correlation, and disregarding this characteristic can lead to deviations in the analysis results. While some systems attempt to introduce spatio-temporal analysis methods, most stay at the level of simple spatio-temporal interpolation or cluster analysis, it is difficult to reveal the nature of the ecological process deeply.
Furthermore, existing systems also suffer from significant drawbacks in terms of predictive capabilities. Most systems can only provide short-term, local predictions, and it is difficult to accurately predict the long-term evolution trend of the ecosystem. This greatly limits the application value of these systems in terms of ecological environment management and decision support.
Finally, existing systems generally lack flexibility and scalability. In the face of ever-changing ecological problems and emerging new data sources, these systems tend to be difficult to adapt and upgrade quickly, resulting in severely limited utility and lifetime.
In view of the above-mentioned shortcomings of the prior art, a new intelligent ecological environment multi-source data collection and analysis system is needed, which can effectively solve a series of key problems such as data fusion, complex system modeling, space-time characteristic analysis, long-term trend prediction and the like. The present invention addresses these desiderata.
Disclosure of Invention
The invention provides an ecological environment multi-source data intelligent acquisition and analysis system and a method thereof, and aims to solve the problems of insufficient data fusion, simple analysis method, limited prediction capability and the like in the prior art. Through innovative system architecture design and advanced algorithm application, the invention realizes the full-flow intelligent processing from data acquisition to deep analysis, and greatly improves the precision and efficiency of ecological environment monitoring and analysis.
In order to solve the technical problems, the invention adopts the following technical scheme:
ecological environment multisource data intelligent acquisition and analysis system, this system includes:
the data acquisition module is used for acquiring multi-source ecological environment data;
the data preprocessing module is used for cleaning and standardizing the multisource 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 presenting analysis results and
And the system management module is used for coordinating the operation of other modules.
Preferably, the data analysis module includes:
The multi-dimensional ecological phase space construction unit is used for constructing an ecological system state representation based on multi-source data;
A topological feature extraction unit for extracting topological features from the ecosystem state representation, and
And the nonlinear dynamics modeling unit is used for establishing an ecosystem dynamics model based on the topological characteristics.
Preferably, the multi-dimensional ecological phase space construction unit obtains a phase space state vector by X t＝Φ(Ft,Ht,St, wherein X t∈Rd is a phase space state vector at time t,For the feature vectors of the multi-source data after fusion,In order to conceal the state vector,As a vector of parameters of the system architecture,Is a nonlinear mapping function, d is a phase space dimension, n f is a feature vector dimension, n h is a hidden state dimension, and n s is a system structure parameter dimension.
Preferably, the topological feature extraction unit obtains the topological feature vector by first calculating a continuous co-ordination: Wherein, the For k sustain a continuation map, PH k maintains a continuation coherent operator for k,For a simplex complex constructed based on the phase space state in the time window w, K is the highest coherent dimension considered, and then, the topological feature vector is extracted: Where T t is the topological feature vector and ψ is the function that converts the persistence map into the feature vector.
Preferably, the nonlinear dynamics modeling unit builds the dynamics model by: Wherein, the As a nonlinear kinetic function, θ is a model parameter, and the specific form is: Wherein A, B, W are weight matrices, c is a bias vector, g (t) is a time dependent external driving function, and E α,β is a Mittaq-Leffler function.
Preferably, the Mittag-Leffer function is defined as: wherein Γ is a gamma function, α, β >0 is a function parameter, and z is a function variable.
Preferably, the nonlinear dynamics modeling unit predicts the future state by: wherein Δt is the predicted time step, τ is the integral variable, and the integral is solved using the adaptive step Runge-Kutta method.
The intelligent ecological environment multi-source data acquisition and analysis method based on the system comprises the following steps:
Acquiring multisource ecological environment data through the data acquisition module;
the data preprocessing module is utilized to clean and standardize the acquired multisource ecological environment data;
storing the preprocessed data in the data storage module;
Deep analysis is carried out on the stored ecological environment data by using the data analysis module;
visually presenting the analysis result through the result display module and
And the system management module coordinates the execution of the steps.
Preferably, the analyzing step of the data analyzing module further includes:
Constructing a multidimensional ecological phase space, and fusing multisource data and system structure information;
extracting topological features based on the constructed phase space, capturing the geometric and topological properties of the system, and
And constructing a nonlinear dynamics model by using topological characteristics to realize ecological system state prediction.
Preferably, the step of constructing the nonlinear dynamics model further includes:
Selecting a Mittag-Leffler function as an activation function, and enhancing the expression capacity of the model on fractional order dynamics;
Comprehensively considering system state, topological characteristics and time dependence to construct a dynamic equation, and
And solving a kinetic equation by using a numerical integration method of the self-adaptive step length to obtain the future state prediction of the system.
The beneficial effects of the invention are mainly represented in the following aspects:
First, from the overall architecture, the system provided by the invention realizes the organic combination and cooperative work of a plurality of functional 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, and each module can fully play the functions and is tightly matched with other modules. The overall design not only improves the operation efficiency of the system, but also enhances the stability and reliability of the system.
In terms of data processing, a great innovation of the invention is to realize deep fusion of multi-source heterogeneous data. By constructing the multidimensional ecological phase space, the system can unify data from different sensors and different scales into one high-dimensional space for analysis. The method effectively solves the problem of data fracture in the traditional system, and lays a solid foundation for subsequent deep analysis.
In the analysis method, the invention introduces advanced technologies such as topology data analysis, nonlinear dynamics modeling and the like. The topological feature extraction unit is capable of capturing geometric and topological information contained in the data, which information often reflects the essential structures and relationships in the ecosystem. The nonlinear dynamics modeling unit greatly enhances the expression capacity of the model on the complex ecological process by introducing a Mittag-Leffler function as an activation function. The combination of the two technologies enables the system to deeply reveal complex interactions in the ecological system, and provides a more reliable scientific basis for ecological environment management.
In terms of predictive power, the system of the present invention constructs a highly accurate kinetic equation by comprehensively considering system states, topological features and time dependencies. By matching with the numerical integration method of the self-adaptive step length, the system not only can accurately predict short-term change, but also can reliably predict the long-term evolution trend of the ecological system. The application value of the system in the aspects of ecological environment planning and decision support is greatly enhanced.
In addition, the system of the invention also exhibits excellent adaptability and expandability. Through the modular design and advanced algorithm framework, the system can easily cope with the access of novel data sources and the new analysis requirements. This means that the system can be continuously upgraded and optimized, maintaining its advancement and practicality for a long period of time.
Finally, from the application effect point of view, the system of the invention is excellent in a plurality of ecological environment monitoring scenes. The system can provide accurate and timely analysis results and early warning information in the fields of forest ecological system monitoring, wetland protection, urban air quality management and the like. This wide applicability makes the system a powerful tool for ecological environmental protection work.
In general, the invention effectively solves a plurality of problems existing in the existing ecological environment monitoring system through innovative system design and advanced algorithm application. The method not only improves the precision and efficiency of the analysis of the ecological environment data, but also provides powerful support for the ecological environment protection and management decision. With the increasing complexity of environmental problems, the value of the invention will be more remarkable, and the invention is expected to play an important role in the global ecological environment protection industry.
Drawings
FIG. 1 is a block diagram of the overall flow of the system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the present invention, and all other embodiments that a person of ordinary skill in the art could achieve without inventive effort are within the scope of the present invention.
As shown in fig. 1, the ecological environment multi-source data intelligent acquisition and analysis system of the invention comprises a data acquisition 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 cooperatively to form a complete data processing flow.
The data acquisition module 1 is responsible for acquiring multisource ecological environment data. In one embodiment of the invention, the module may include various sensors, such as air quality sensors, water quality sensors, soil sensors, etc., distributed in different ecological environments. The sensors can collect various environmental parameters such as temperature, humidity, pH value, pollutant concentration and the like in real time. Preferably, the frequency of data collection may be adjusted according to varying characteristics of different environmental parameters, for example, air quality data may need to be collected once an hour, while soil data may be collected once a day.
The data preprocessing module 2 cleans and standardizes the acquired multisource ecological environment data. This step is critical because the raw data may contain noise, outliers or missing values. The invention adopts a self-adaptive data cleaning algorithm, and can automatically adjust cleaning parameters according to the characteristics of different data sources. For example, for temperature data, a reasonable variation range, such as-50 ℃ to 50 ℃, can be set, and data outside this range can be marked as abnormal and processed.
The data storage module3 is used for storing the preprocessed ecological environment data. In consideration of diversity and large-scale nature of ecological environment data, the invention adopts a distributed storage system, and can effectively process large-scale heterogeneous data. Preferably, the data is stored in a time series form, facilitating subsequent timing analysis.
The data analysis module 4 is the core of the invention and is used for carrying out deep analysis on the stored ecological environment data.
In a preferred embodiment of the invention, the module comprises a multidimensional ecophase space building unit 41, a topological feature extraction unit 42 and a nonlinear dynamics modeling unit 43. The cooperation of the three units enables the system to deeply understand the dynamic changes of the complex ecological system.
In one embodiment of the present invention, the multidimensional ecological phase space constructing unit 41 obtains the phase space state vector by:
Xt＝Φ(Ft,Ht,St)
Wherein X t∈Rd is a phase space state vector at time t, For the feature vectors of the multi-source data after fusion,In order to conceal the state vector,As a vector of parameters of the system architecture, Is a nonlinear mapping function.
In practical applications, the choice of d depends on the complexity of the ecosystem, and values between 50 and 200 can be chosen in general. n f may be between 10 and 50 depending on the number of environmental parameters acquired. The choice of n h and n s needs to be determined experimentally, typically n h can be 1.5 to 2 times n f and n s can be 0.5 to 1 times n f.
In one embodiment of the present invention, the topology feature extraction unit 42 first calculates the continuous co-ordinates:
Wherein, the For k sustain a continuation map, PH k maintains a continuation coherent operator for k,Is a simplex complex constructed based on the phase space state within the time window w. Then, the topological feature vector is extracted:
Where T t is the topological feature vector and ψ is the function that converts the persistence map into the feature vector. In the analysis of the ecological environment, the selection of the time window w is very important. For fast changing parameters (e.g. air quality) w may be chosen for 24 hours, and for slow changing parameters (e.g. soil quality) w may be chosen for a week or more. The choice of K is typically not more than 3, since high-dimensional coherent information is not significant in a physiological system.
In one embodiment of the present invention, the nonlinear dynamics modeling unit 43 builds a dynamics model by:
The nonlinear dynamics modeling unit 43 builds a dynamics model by:
Wherein, the And θ is a model parameter as a nonlinear kinetic function. The specific form is as follows:
Wherein A, B, W are weight matrices, c is a bias vector, g (t) is a time dependent external driving function, and E α,β is a Mittag-Leffer function.
Such nonlinear dynamics models are capable of capturing 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 number, and conventional linear models are difficult to describe accurately. The model of the invention enhances the expression capability of the complex relationship by introducing a Mittag-Leffer function as an activation function.
Preferably, the values of α and β can be determined by cross-validation, typically α is between 0.5 and 1 and β is between 0.1 and 0.5. This choice can avoid the over-fitting problem while maintaining the model expression ability.
The result display module 5 is responsible for visually presenting the analysis results. The invention adopts an interactive visualization technology, so that an on-state student and an environment manager can intuitively understand the complex ecological system dynamics. For example, interactions between different environmental factors may be demonstrated by 3D graphics, or long-term trends in the ecosystem may be demonstrated by time series diagrams.
The system management module 6 coordinates the operation of the other modules to ensure efficient operation of the overall system. The system is also responsible for the functions of user authority management, system log record and the like, and ensures the safety and traceability of the system.
The ecological environment multi-source data intelligent acquisition and analysis system realizes the full-flow intelligent processing from data acquisition to deep analysis through the cooperative work of the modules. Compared with the traditional method, the system has the advantages that firstly, the fusion and the depth analysis of the multi-source data improve the comprehensiveness and the accuracy of ecological environment assessment, secondly, the topological feature extraction can capture complex structural relations in an ecological system, and finally, the nonlinear dynamics modeling improves the prediction capability of the ecological system on future states.
In practical application, the system can be used for various ecological environment monitoring and early warning scenes. For example, in forest ecosystem monitoring, various factors such as air temperature, precipitation, soil humidity, vegetation coverage rate and the like can be comprehensively analyzed to predict forest fire risks or possibility of pest outbreaks. In the protection of the wetland ecosystem, the health condition of the wetland can be estimated and protection suggestions can be made by analyzing parameters such as water level change, pollutant concentration, biodiversity index and the like.
In a word, the ecological environment multi-source data intelligent acquisition and analysis system and the method thereof provide powerful technical support for ecological environment protection and management through innovative data processing and analysis technology, and are expected to play an important role in coping with global ecological environment challenges.
In one embodiment of the present invention, the Mittag-Leffer function used in the present invention is defined as:
Wherein Γ is a gamma function, α, β >0 is a function parameter, and z is a function variable. The Mittag-Leffer function is an important special function and has wide application in fractional calculus and complex system modeling. The Mittag-Leffer function is introduced as an activation function in the present invention mainly considering that the ecosystem often exhibits fractional order dynamics.
For example, in studying the carbon circulation of a forest ecosystem, the growth and carbon absorption of trees do not follow a simple integer-order differential equation, but rather exhibit fractional order characteristics. By using the Mittag-Leffler function, our model is able to capture this complex dynamic process more accurately. In practical applications, the selection of α and β needs to be adjusted according to specific ecosystem characteristics, and preferably, optimal α and β values may be found on the training data by a grid search method or the like.
Notably, the computation of the Mittag-Leffer function may be more complex than conventional activation functions (e.g., reLU or sigmoid). In order to improve the calculation efficiency, the invention adopts an approximate calculation method based on the fast Fourier transform, thereby greatly reducing the calculation complexity and enabling the function to be applied in a real-time system.
In one embodiment of the present invention, the nonlinear dynamics modeling unit 43 predicts the future state by
Where Δt is the predicted time step and τ is the integral variable. This integral equation is essentially an initial problem that describes the evolution of the system state over time. In practical application, we solve the above integral using the adaptive step-size finger-Kutta method.
The selection of the adaptive step size is important for improving the calculation efficiency and ensuring the numerical stability. In a preferred embodiment of the present invention we use Dormand-Prince method (also called RKDP method) as a specific implementation of the adaptive step-size range-Kutta method. The method can automatically adjust the step length according to the local truncation error, and improves the calculation efficiency while ensuring the precision.
For example, when the water level of the wetland is predicted to change, the algorithm automatically increases the step length and reduces the calculated amount when the water level changes smoothly, and when the water level changes suddenly due to heavy rain and the like, the algorithm automatically reduces the step length to capture the dynamic process of rapid change. This adaptive nature enables the present system to efficiently handle ecological processes on various time scales.
In a preferred embodiment of the invention, the invention further provides an ecological environment multi-source data intelligent acquisition and analysis method based on the system. The method comprises the steps of acquiring multi-source ecological environment data through a data acquisition module 1, cleaning and standardizing the acquired multi-source ecological environment data through a data preprocessing module 2, storing the preprocessed data in a data storage module 3, performing deep analysis on the stored ecological environment data through a data analysis module 4, visually presenting analysis results through a result display module 5, and coordinating execution of the steps through a system management module 6.
An important feature of this method is its nature of loop iteration. In practical applications, ecological environment data is continuously generated, and the system needs to continuously collect, process and analyze the data. Therefore, the steps form a closed loop, and the system can continuously run to continuously update and optimize the analysis result.
For example, when urban air quality monitoring is performed, the system continuously collects air quality data of each monitoring point and updates analysis results in real time. If an abnormally elevated contaminant concentration in a region is detected, the system will immediately alert while a finer analysis process is initiated, such as increasing the data sampling frequency, invoking a more complex predictive model, etc.
In the preferred embodiment of the invention, the analysis step of the data analysis module 4 further comprises the steps of constructing a multidimensional ecological phase space, fusing multisource data and system structure information, extracting topological features based on the constructed phase space, capturing the geometric and topological properties of the system, and constructing a nonlinear dynamics model by utilizing the topological features to realize ecological system state prediction.
These three steps constitute the main flow of the core algorithm of the present invention. Notably, there is a close link and feedback mechanism between these three steps. For example, the results of the topological feature extraction can adversely affect the construction of the phase space, and the predicted results of the kinetic model can also lead to a re-evaluation of the phase space structure.
In practice, this feedback mechanism enables the system to constantly self-optimize. For example, in monitoring a forest ecosystem, if a significant difference exists between the tree growth rate predicted by the model and the actual observed value, the system can automatically adjust the construction mode of the phase space, and possibly add a new dimension (such as introducing a soil microbial activity index) or adjust the weight of the existing dimension.
In the preferred embodiment of the invention, the construction step of the nonlinear dynamics model further comprises the steps of selecting a Mittag-Leffler function as an activation function, enhancing the expression capability of the model on fractional dynamics, comprehensively considering the system state, topological characteristics and time dependence, constructing a dynamics equation, and solving the dynamics equation by using a numerical integration method of an adaptive step length to obtain the future state prediction of the system.
This step is the core of the whole analysis process and is also the most innovative part of the present invention. By introducing Mittag-Leffler functions and topological features, the model of the invention can capture complex ecological processes that are difficult to describe by traditional methods. For example, in studying marine ecosystems, it is often difficult for traditional models to accurately describe the colonial behavior of fish shoals and their impact on the ecosystem. By introducing topological features, the model can effectively capture the spatial structural features of the fish shoal, so that the influence of the model on an ecological system can be predicted more accurately.
In addition, the numerical integration method of the self-adaptive step length not only improves the calculation efficiency, but also enhances the adaptability of the model to ecological processes of different time scales. For example, in the same model, we can predict both fast-changing weather factors (e.g., rainfall) and slow-changing ecological factors (e.g., vegetation coverage) at the same time, without building separate models for different time scales.
In general, the ecological environment multi-source data intelligent acquisition and analysis system and the method thereof provided by the invention realize the efficient and accurate analysis and prediction of a complex ecological system through innovative algorithm design and system architecture. The system can be applied to environment monitoring and ecological protection, and can also provide important decision support for the fields of urban planning, agricultural production and the like. In the future, with the further development of the Internet of things technology and artificial intelligence algorithms, the system has a large space for optimization and expansion, and is expected to play a more important role in coping with global ecological environment challenges.
The above description is only of the preferred embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to substitute or change the scheme according to the present invention and its modified concept within the scope of the present invention.

Claims (5)

1.生态环境多源数据智能采集与分析系统，其特征在于，该系统包括：1. An intelligent ecological environment multi-source data collection and analysis system, characterized in that the system includes: 数据采集模块，用于获取多源生态环境数据；Data acquisition module, used to obtain multi-source ecological environment data; 数据预处理模块，用于对所述多源生态环境数据进行清洗和标准化处理；A data preprocessing module, used for cleaning and standardizing the multi-source ecological environment data; 数据存储模块，用于存储经过预处理的生态环境数据；A data storage module, used to store pre-processed ecological environment data; 数据分析模块，用于对所述存储的生态环境数据进行深度分析；A data analysis module, used to perform in-depth analysis on the stored ecological environment data; 结果展示模块，用于可视化呈现分析结果；以及Result display module, used to visualize the analysis results; and 系统管理模块，用于协调其他模块的运行；System management module, used to coordinate the operation of other modules; 所述数据分析模块包括：The data analysis module includes: 多维生态相空间构建单元，用于基于多源数据构建生态系统状态表示；A multidimensional ecological phase space construction unit for constructing ecosystem state representations based on multi-source data; 拓扑特征提取单元，用于从所述生态系统状态表示中提取拓扑特征；以及a topological feature extraction unit, configured to extract topological features from the ecosystem state representation; and 非线性动力学建模单元，用于基于所述拓扑特征建立生态系统动力学模型；a nonlinear dynamics modeling unit, configured to establish an ecosystem dynamics model based on the topological features; 所述多维生态相空间构建单元通过以下方式获得相空间状态向量：Xt＝Φ(Ft,Ht,St)，其中，Xt∈Rd为t时刻的相空间状态向量，为融合后的多源数据特征向量，为隐藏状态向量，为系统结构参数向量，为非线性映射函数，d为相空间维度，nf为特征向量维度，nh为隐藏状态维度，ns为系统结构参数维度；The multi-dimensional ecological phase space construction unit obtains the phase space state vector in the following manner: X 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, nf is the feature vector dimension, nh is the hidden state dimension, and ns is the system structure parameter dimension; 所述拓扑特征提取单元通过以下方式获得拓扑特征向量：首先，计算持续同调：其中，为k维持续图，PHk为k维持续同调算子，为基于时间窗口w内相空间状态构建的单纯复形，K为考虑的最高同调维数；然后，提取拓扑特征向量： 其中，Tt为拓扑特征向量，Ψ为将持续图转化为特征向量的函数；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 persistence 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: Where T t is the topological eigenvector, and Ψ is the function that converts the persistence graph into the eigenvector; 所述非线性动力学建模单元通过以下方式建立动力学模型： 其中，为非线性动力学函数，θ为模型参数，具体形式为：其中，A,B,W为权重矩阵，c为偏置向量，g(t)为时间依赖的外部驱动函数，Eα,β为Mittaq-Leffler函数；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: Where A, B, W are weight matrices, c is the bias vector, g(t) is the time-dependent external driving function, and E α, β are Mittaq-Leffler functions; 所述Mittag-Leffler函数定义为：其中，Γ为伽马函数，α,β>0为函数参数，z为函数变量。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.根据权利要求1所述的系统，其特征在于，所述非线性动力学建模单元通过以下方式预测未来状态：其中，Δt为预测时间步长，τ为积分变量，使用自适应步长的Runge-Kutta方法求解上述积分。2. The system according to claim 1, wherein 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. 3.基于权利要求1-2任一项所述系统的生态环境多源数据智能采集与分析方法，其特征在于，该方法包括以下步骤：3. A method for intelligently collecting and analyzing multi-source ecological environmental data based on the system according to any one of claims 1 to 2, characterized in that the method comprises the following steps: 通过所述数据采集模块获取多源生态环境数据；Acquire multi-source ecological environment data through the data acquisition module; 利用所述数据预处理模块对获取的多源生态环境数据进行清洗和标准化处理；Using the data preprocessing module to clean and standardize the acquired multi-source ecological environment data; 将预处理后的数据存储在所述数据存储模块中；storing the preprocessed data in the data storage module; 使用所述数据分析模块对存储的生态环境数据进行深度分析；Use the data analysis module to conduct in-depth analysis on the stored ecological environment data; 通过所述结果展示模块可视化呈现分析结果；以及Visually present the analysis results through the result display module; and 由所述系统管理模块协调上述各步骤的执行。The system management module coordinates the execution of the above steps. 4.根据权利要求3所述的方法，其特征在于，所述数据分析模块的分析步骤进一步包括：4. The method according to claim 3, wherein the analysis step of the data analysis module further comprises: 构建多维生态相空间，融合多源数据和系统结构信息；Construct a multi-dimensional ecological phase space, integrating multi-source data and system structure information; 基于构建的相空间提取拓扑特征，捕捉系统的几何和拓扑性质；以及Extract topological features based on the constructed phase space to capture the geometric and topological properties of the system; and 利用拓扑特征构建非线性动力学模型，实现生态系统状态预测。Topological features are used to construct nonlinear dynamic models to predict ecosystem status. 5.根据权利要求4所述的方法，其特征在于，所述非线性动力学模型的构建步骤进一步包括：5. The method according to claim 4, wherein the step of constructing the nonlinear dynamic model further comprises: 选择Mittag-Leffler函数作为激活函数，增强模型对分数阶动力学的表达能力；The Mittag-Leffler function is selected as the activation function to enhance the model's ability to express fractional-order dynamics; 综合考虑系统状态、拓扑特征和时间依赖性，构建动力学方程；以及使用自适应步长的数值积分方法求解动力学方程，获得系统未来状态预测。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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