Dynamic ecological environment monitoring system based on machine learning

Abstract

The present invention discloses an ecological environment dynamic monitoring system based on machine learning, which relates to the field of artificial intelligence technology; a sensor monitoring part, a feature extraction and mapping part, a support vector analysis and decision part, and a prediction risk assessment part; the sensor monitoring part includes a sample ecological area sensor group and a target ecological area sensor group; the sample ecological area sensor group includes a plurality of identical sample sensor groups; the target ecological area sensor group includes only one target sensor group identical to the sample sensor group; the feature extraction and mapping part is used to obtain mapping features; a dynamic system state reconstruction equation is constructed; the support vector analysis and decision part is used to construct an optimal support vector prediction controller; the prediction risk assessment part determines whether an ecological environment early warning is needed according to the prediction judgment value. The present invention realizes comprehensive monitoring, precise modeling, and intelligent early warning of the ecological environment.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an ecological environment dynamic monitoring system based on machine learning. Background Art

[0002] With the rapid development of industrialization and urbanization, the impact of human activities on the ecological environment has become increasingly severe. Environmental pollution problems on a global scale have become a major challenge threatening the stability of ecosystems and the sustainable development of mankind. The combined effects of water pollution, air pollution and soil pollution have not only led to the degradation of ecosystem functions, but also had a profound impact on biodiversity and human health. In order to cope with increasingly complex environmental problems, countries have invested resources in the development of ecological environment monitoring technologies. However, existing environmental monitoring methods still have significant technical bottlenecks when facing the multidimensional dynamic changes of ecosystems, and it is difficult to meet the actual needs of modern environmental protection and management.

[0003] At present, traditional environmental monitoring technologies mainly focus on single-medium monitoring and static data analysis. For example, in the field of water environment monitoring, the monitoring focus is usually on indicators such as dissolved oxygen, chemical oxygen demand, biological oxygen demand, total phosphorus and total nitrogen, which are usually obtained through manual regular sampling and laboratory analysis. However, this method has significant limitations: on the one hand, the temporal and spatial coverage of manual sampling is limited, and it is difficult to fully reflect the dynamic changes of pollutants in water bodies; on the other hand, the laboratory analysis cycle is long, and it is impossible to achieve real-time monitoring and response to sudden pollution events. In atmospheric environmental monitoring, although monitoring equipment can provide real-time collection of data such as PM2.5, sulfur dioxide, and nitrogen oxides, these data are usually based on point monitoring at fixed sites, and it is difficult to accurately describe the migration and diffusion laws of pollutants in the atmosphere. Soil monitoring faces more challenges. Due to the cumulative effect and spatial heterogeneity of soil pollutants, the assessment of its pollution status usually needs to rely on large-scale manual sampling and chemical analysis, which has a long cycle and high cost, and it is difficult to meet the requirements of dynamic monitoring.

[0004] In addition, the existing environmental monitoring system also has significant deficiencies in data processing and analysis. Many traditional methods rely on linear models or simple statistical analysis methods, such as multiple regression analysis and time series prediction models. Although these methods perform well in static predictions of single variables, they are limited in their capabilities when faced with the nonlinear dynamic characteristics and multivariate coupling relationships of ecosystems. For example, the diffusion of pollutants in water bodies is not only affected by hydrodynamic conditions, but is also closely related to temperature, pH value and other biochemical factors, and these complex relationships are difficult to effectively model using traditional linear methods. Similarly, in the atmospheric environment, changes in pollutant concentrations are affected by the combined effects of meteorological conditions (such as wind speed, humidity and temperature) and human activities (such as industrial emissions and traffic pollution). This multidimensional nonlinear dynamic characteristic cannot be accurately captured by traditional methods. Summary of the invention

[0005] The purpose of the present invention is to provide an ecological environment dynamic monitoring system based on machine learning, which significantly improves the dynamic analysis capability of complex ecosystems. Compared with the prior art, the beneficial effects of the present invention are reflected in the substantial improvement of real-time performance, accuracy and adaptability, and can achieve efficient integration of multi-media data, timely response to sudden environmental events and intelligent risk management, providing strong technical support for ecological environment protection and sustainable development.

[0006] In order to solve the above technical problems, the present invention provides an ecological environment dynamic monitoring system based on machine learning, the system comprising: a sensor monitoring part, a feature extraction and mapping part, a support vector analysis and decision-making part and a predictive risk assessment part; the sensor monitoring part comprises a sample ecological area sensor group and a target ecological area sensor group; the sample ecological area sensor group comprises a plurality of identical sample sensor groups, each sample sensor group acquires air data, water quality data and soil data of the sample ecological area in real time to form a sensor data vector; the target ecological area sensor group comprises only one target sensor group that is the same as the sample sensor group, acquires air data, water quality data and soil data of the sample ecological area in real time to form a target data vector; the feature extraction and mapping part is used to analyze all sample sensor groups at the current time. The sensor data vectors previously acquired are cross-mapped based on the kernel function to obtain mapping features; based on the mapping features, a dynamic system state reconstruction equation is constructed to simulate the changes in the target ecological area, and the dynamic system state reconstruction equation is solved to obtain the state change vector; the support vector analysis and decision-making part is used to take the state change vector as an input value, construct a Lyapunov function based on a support vector machine, and perform entropy value dynamic evolution analysis on the value of the Lyapunov function to obtain the entropy evolution function; the state change vector and the entropy evolution function are combined, and then according to the entropy evolution function, an optimal support vector prediction controller is constructed; the prediction risk assessment part is used to take the target data vector as an input vector, input it into the optimal support vector prediction controller, generate a prediction judgment value, and judge whether an ecological environment early warning is needed according to the prediction judgment value.

[0007] Furthermore, the absolute value of the difference in altitude between the sample ecological region and the target ecological region is less than the set altitude similarity threshold; the absolute value of the difference in humidity, the absolute value of the difference in temperature, and the absolute value of the difference in soil moisture content between the sample ecological region and the target ecological region within a set time range are respectively less than their respective corresponding similarity thresholds; at the same time, the sample ecological region and the target ecological region have the same vegetation type; the sample ecological region is an ecological region that is determined to have water pollution, soil pollution, and air pollution.

[0008] Furthermore, the air data includes: sulfur dioxide concentration, nitrogen oxide concentration, ozone concentration, PM2.5 concentration, temperature and humidity; the water quality data includes: dissolved oxygen content, chemical oxygen demand, biological oxygen demand, total phosphorus, total nitrogen, water pH value and turbidity; the soil data includes: soil moisture content, soil pH value, organic matter content, soil temperature, lead content, cadmium content, mercury content and salt concentration.

[0009] Furthermore, the sensor data vectors acquired by all sample sensor groups before the current time are cross-mapped based on the kernel function to obtain the mapping features through the following formula:

[0010] ;

[0011] in, is the mapping feature; Indicates The sample sensor group and The distance between the sample sensor groups; is the number of sample sensor groups; and All are integer subscript indices; For the The sample sensor group The sensor data vector acquired at the time; For the The sample sensor group The sensor data vector acquired at the time; is the Hilbert-Schmidt operator; each sensor data vector is composed of three sub-component vectors, namely: a moisture component vector corresponding to water quality data, an air component vector corresponding to air data, and a soil component vector corresponding to soil data.

[0012] Furthermore, the state reconstruction equation of the dynamic system is constructed through the following formula:

[0013] ;

[0014] in, is the state change vector; is the divergence operator; is the Laplace operator; for The determinant of ; for traces; is the air pollution diffusion coefficient, , is the Boltzmann constant, is the temperature, is the air viscosity, is the short path of the sample ecoregion; is the water pollution diffusion coefficient, and its value range is arrive ; for arrive ; for The air component vector of the mean vector of all sensor data vectors acquired at the time; for The moisture component vector of the mean vector of all sensor data vectors acquired at the time; for The soil component vector of the mean vector of all sensor data vectors acquired at time.

[0015] Furthermore, the Lyapunov function based on the support vector machine is constructed by the following formula:

[0016] ;

[0017] in, is the state change vector No. Quantity; is the curl operator; is the tensor product; is the Lyapunov function; is the L1 norm; Transpose operation for vectors or matrices.

[0018] Furthermore, the entropy evolution function is obtained by analyzing the dynamic evolution of the Lyapunov function through the following formula:

[0019] ;

[0020] in, is the entropy evolution function.

[0021] Furthermore, the optimal support vector predictive controller is constructed through the following formula:

[0022] ;

[0023] ;

[0024] ;

[0025] in, is the objective function value of the support vector; by minimizing the objective function, the discriminant correction value is obtained ; Indicates the constraints that are satisfied; is the target data vector, and also serves as the input vector; is the predicted judgment value.

[0026] Furthermore, if the predicted judgment value exceeds the set threshold, an ecological environment warning will be issued.

[0027] The ecological environment dynamic monitoring system based on machine learning of the present invention has the following beneficial effects: the present invention realizes multi-media comprehensive monitoring by combining multi-dimensional environmental variables of three key ecological media: air, water and soil through sensor monitoring modules. The system can collect air data (such as PM2.5, sulfur dioxide, nitrogen oxide concentration, etc.), water quality data (such as total phosphorus, total nitrogen, dissolved oxygen, etc.) and soil data (such as heavy metal content, organic matter content, etc.) in real time to form a multi-dimensional data vector. This design breaks through the single medium limitation of traditional monitoring methods and ensures the dynamic capture of complex interactions between media in the ecosystem. For example, when the deposition of atmospheric pollutants affects the eutrophication of water bodies, the system can simultaneously monitor the relevant parameters of the atmosphere and water bodies, providing comprehensive data support for the comprehensive analysis of pollution sources and propagation paths. This multi-media collaborative monitoring significantly improves the comprehensiveness of the monitoring system and can more realistically reflect the overall state of the ecological environment. The present invention uses feature extraction and mapping modules and dynamic system state reconstruction equations to model the dynamic changes of the ecological environment with high precision. The feature extraction part maps the sensor data vector to a high-dimensional space based on the cross-mapping method of the kernel function, thereby capturing the nonlinear characteristics in the sample data. The state reconstruction equation of the dynamic system further combines mathematical tools such as divergence and Laplace operator to simulate the dynamic diffusion law of pollutants and the time evolution characteristics of the ecosystem. For example, in the analysis of water pollution diffusion, the equation can accurately simulate the migration path and diffusion speed of pollutants in the water body, thereby predicting the future pollution situation. The accuracy of this nonlinear dynamic modeling not only improves the system's ability to understand complex ecosystems, but also provides a more reliable basis for risk assessment. The present invention realizes the real-time prediction and evaluation of the ecological environment state through support vector analysis and decision modules and optimal support vector predictive controllers. The support vector analysis part dynamically evaluates the stability and sustainability of the ecosystem by constructing a stability analysis framework based on the Lyapunov function; the optimal support vector predictive controller optimizes the objective function to improve the calculation efficiency while ensuring the prediction accuracy. For example, when facing a sudden pollution incident, the system can quickly generate a prediction judgment value based on the real-time collected data, and adjust the parameters through the optimized controller to adapt to the state changes caused by the sudden incident. This real-time prediction capability ensures that the system can respond to the dynamic changes of the environment in a timely manner, and provides an efficient solution for the response to sudden environmental events. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0029] Figure 1 A schematic diagram of the system structure of an ecological environment dynamic monitoring system based on machine learning provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Example 1, reference Figure 1 : An ecological environment dynamic monitoring system based on machine learning, the system includes: a sensor monitoring part, a feature extraction and mapping part, a support vector analysis and decision-making part and a predictive risk assessment part; the sensor monitoring part includes a sample ecological area sensor group and a target ecological area sensor group; the sample ecological area sensor group includes a plurality of identical sample sensor groups, each sample sensor group acquires air data, water quality data and soil data of the sample ecological area in real time to form a sensor data vector; the target ecological area sensor group includes only one target sensor group that is the same as the sample sensor group, acquires air data, water quality data and soil data of the sample ecological area in real time to form a target data vector; the feature extraction and mapping part is used to perform a multi-sample sensor analysis on the data acquired by all sample sensor groups before the current time. The sensor data vector is cross-mapped based on the kernel function to obtain mapping features; based on the mapping features, a dynamic system state reconstruction equation is constructed to simulate the changes in the target ecological area, and the dynamic system state reconstruction equation is solved to obtain the state change vector; the support vector analysis and decision-making part is used to take the state change vector as an input value, construct a Lyapunov function based on a support vector machine, and perform entropy value dynamic evolution analysis on the value of the Lyapunov function to obtain the entropy evolution function; the state change vector and the entropy evolution function are combined, and then according to the entropy evolution function, an optimal support vector prediction controller is constructed; the prediction risk assessment part is used to take the target data vector as an input vector, input it into the optimal support vector prediction controller, generate a prediction judgment value, and judge whether an ecological environment early warning is needed according to the prediction judgment value.

[0032] Specifically, the setting of the sample ecological region sensor group is representative and diverse, ensuring the wide coverage of data collection. The sample sensor group consists of multiple identical collection units, each of which simultaneously monitors key variables of the ecological environment such as air quality parameters, water quality indicators and soil characteristics. This design forms a cross-regional multidimensional data network by sampling data from different ecological regions in spatial distribution. The sample ecological region sensor group not only forms a multi-point distribution in geographic space, but also has temporal and diverse data vectors, which enables it to reflect the dynamic changes and complex characteristics of the ecosystem. The core advantage of the sample data is that it can not only capture regional environmental characteristics, but also extract change trends through comparative analysis of inter-group data, providing sufficient data support for subsequent modeling. Secondly, the design of the target ecological region sensor group is simplified and accurate, with the aim of reflecting the ecological and environmental status of a single region in real time. The setting of the target region sensor group is consistent with that of the sample sensor group, but its uniqueness lies in its focus on dynamic monitoring of specific ecological regions. It collects the core variables of air, water quality and soil in real time and generates target data vectors, providing a basis for subsequent dynamic monitoring and risk assessment. The data of the target sensor group is not only real-time, but also compared and analyzed with the data features of the sample sensor group, so as to realize the dynamic association and systematic modeling between ecological regions. The core principle of the sensor monitoring part is also reflected in the generation and optimization of data vectors. The air quality, water quality and soil data collected by each sensor group are orderly integrated into multidimensional vectors. This data vectorization processing method reflects the design ideas of data structuring and model adaptability. Specifically, air quality data includes the concentration of fine particulate matter (such as PM2.5, PM10), the concentration of gas components such as carbon dioxide and ozone and their changes; water quality data covers pH value, dissolved oxygen content, total nitrogen and total phosphorus concentration, etc.; soil data focuses on organic matter content, heavy metal indicators and microbial community distribution. These data are compressed into a structured vector representation through the precise collection of sensors, which not only simplifies the storage and processing of data, but also facilitates the subsequent feature extraction module to perform unified kernel function mapping. In addition, the sensor monitoring part is highly intelligent in the collaborative design of time and space. In space, the multi-point distribution of the sample sensor group and the single-point precise collection of the target sensor group form a complementary relationship. The sample data captures macro-ecological characteristics, while the target data focuses on micro-regional dynamics, thus achieving multi-scale monitoring of the ecological environment. In terms of time, the sensor group can reflect the dynamic changes of the ecosystem in real time through high-frequency data sampling. This high timeliness provides stable data input for subsequent dynamic monitoring and prediction.

[0033] The feature extraction and mapping part is based on the kernel function. By introducing the kernel method to map the sensor data vector, it can effectively capture nonlinear features and express them as vectors in the high-dimensional feature space. This mapping process breaks the limitations of traditional linear analysis methods in dealing with complex systems and can fully explore the nonlinear dynamic characteristics in the ecological environment system. The multidimensional environmental data such as air quality, water quality, and soil collected by the sensor monitoring part are transformed by the kernel function during the mapping process. As a result, these original variables are projected into the high-dimensional space to achieve a clearer and more intuitive feature expression. For example, when analyzing air pollution data, the kernel function can help reveal the implicit relationship between changes in air pollutant concentrations and meteorological conditions. These nonlinear features are often difficult to show in the original data space. The mapped feature vector not only contains the independent information of each variable, but also integrates the complex interactive relationship between them, providing a higher-dimensional description for the state expression of the ecological environment system. In the high-dimensional feature space, the feature extraction and mapping part further dynamically analyzes the mapping features through the dynamic system state reconstruction equation. The basic idea of ​​dynamic system state reconstruction is to simulate the dynamic change process of the target ecological area through the time series association of historical data. This process is based on the time series superposition analysis of the sensor group data of the sample ecological region. By constructing the state reconstruction equation, the transition process of the ecosystem from one state to the next state is described. Specifically, the core of the state reconstruction equation is to establish a mapping relationship between the historical characteristics of environmental variables and their future evolution trends, so that the system can not only explain past changes, but also predict future states. For example, when the air quality data in the sample area changes significantly, the reconstruction equation can capture the dynamic characteristics of pollutant diffusion and reveal its possible impact on the target area through feature mapping. Another important aspect of the state reconstruction of the dynamic system is to extract the state change vector using the data after feature mapping. The purpose of this vectorization processing is to compress the complex dynamic characteristics of the system into controllable and solvable state change indicators, so that the dynamic changes of the ecological environment can be represented by a finite set of state vectors. The state change vector can not only reflect the state of the system at different time nodes, but also quantify the evolution trend of these states. For example, when the soil data of an ecological region changes from rich in organic matter to excessive heavy metal pollution, the state change vector can represent this dynamic process in a quantitative form and provide direct input for subsequent risk assessment. In addition, another core contribution of the feature extraction and mapping part is its dimensionality reduction processing and information optimization of data features. By combining kernel function mapping and state reconstruction equations, the system can extract the most representative features in the high-dimensional feature space, while filtering out redundant information and noise, thereby improving the quality and availability of data. This process is particularly important in ecological and environmental monitoring, because environmental data is often interfered by randomness and uncertainty, such as the impact of sensor errors or short-term climate fluctuations on data.Through the processing of feature extraction and mapping, the system can effectively suppress these interferences and ensure that the data input into the subsequent support vector analysis module has high accuracy and high stability.

[0034] The first step of support vector analysis is to construct a Lyapunov function based on support vector machine with the state change vector as input to evaluate the stability of the ecosystem. The core role of the Lyapunov function is to provide a mathematical stability measure for the dynamic changes of the ecological environment. It analyzes the degree and trend of the system state deviation from the equilibrium point to evaluate whether the ecosystem can remain stable or gradually restore balance under external interference. In the dynamic monitoring of the ecological environment, the state of the system is often subject to complex interference from multiple factors, such as sudden pollution events, climate change or uncertainty caused by human activities. By constructing the Lyapunov function through support vector machine, the characteristics of the kernel function can be effectively combined to map the multidimensional nonlinear state changes into a unified stability assessment framework. This process not only considers the independent role of each variable, but also integrates the complex interaction between variables, thus providing an efficient and rigorous tool for the dynamic analysis of the ecosystem. Based on the results of the Lyapunov function, the support vector analysis and decision-making part further introduces the entropy value dynamic evolution analysis to quantify the complexity of the state change of the ecological environment. Entropy is a mathematical representation of the degree of disorder of the system, and its dynamic evolution process reflects the stability and evolution trend of the ecosystem in the time dimension. In this system, the role of the dynamic evolution analysis of entropy is to reveal the evolution process of the ecosystem from order to disorder or from disorder to order by tracking the probability distribution and complexity changes of state changes. For example, when an ecological area is polluted, its environmental state may show the characteristics of rapid change, which is manifested as a significant increase in the evolution curve of entropy value; on the contrary, when the system gradually returns to a balanced state, the evolution of entropy value tends to be stable or decrease. Through the quantitative analysis of the dynamic change of entropy value, the system can identify the potential instability risk of the environmental system and provide key information for subsequent prediction and intervention. After completing the dynamic evolution analysis of entropy value, the support vector analysis and decision-making part further uses the state change vector and entropy value evolution function to construct the optimal support vector predictive controller. The design goal of this controller is to form an intelligent control unit that can accurately predict the future state of the ecosystem by optimizing the parameters of the support vector machine model. In the optimization process of the controller, the entropy value evolution function is not only used as part of the objective function to measure the state complexity of the system, but also plays a key role in the dynamic adjustment of the controller. For example, when the dynamic evolution function of the entropy value indicates that the system has entered a high-risk state, the controller can automatically adjust its prediction parameters to improve its sensitivity to system mutations; while in a low-risk state, the controller pays more attention to the accurate prediction of long-term trends. This prediction mechanism based on dynamic risk adjustment makes the controller show significant advantages in adaptability and robustness.

[0035] The prediction risk assessment part takes the target data vector as input. Its core principle is to integrate the air, water quality and soil data collected by the sensors in the target area in real time into a unified multi-dimensional input vector. These data reflect the immediate ecological state of the target area and are a real-time depiction of the current environmental characteristics. The generation process of the target data vector has been optimized by the previous sensor monitoring part to ensure the high accuracy and low noise characteristics of the data. At the same time, through the structured vector expression, the adaptability to the support vector predictive controller is improved. At this stage, the data must not only reflect the current state of the target area, but also contain enough information to predict future changes. This requires the system to accurately capture the dynamic characteristics and potential trends of the ecological environment in the target area. Next, the target data vector is input into the optimal support vector predictive controller, which is the core operation link of the prediction risk assessment part. The role of the support vector predictive controller is to use machine learning methods to dynamically predict the future state of the target area. Its design is based on the previous support vector analysis and decision module, combined with the dynamic evolution results of the Lyapunov function and entropy value, to achieve the optimal parameter adjustment of the controller. Through the support vector machine model, the predictive controller can identify the nonlinear patterns and complex associations of the target data in the high-dimensional feature space and map these patterns into the future evolution trend of the system state. The key to this process is that the controller has the ability to capture long-term trends and short-term changes by learning from historical data, so that it can provide accurate risk prediction in the complex ecological environment. Based on the prediction judgment value generated by the support vector predictive controller, the system enters the core link of risk assessment. The prediction judgment value is the risk characterization result of the target data vector under the current system parameters. It not only contains the trend prediction of the future state of the target area, but also combines the dynamic analysis results of the entropy value inside the controller to evaluate the uncertainty of the future state and the possibility of system instability. When the prediction judgment value exceeds the preset risk threshold, the system will judge that there is a potential ecological and environmental threat in the target ecological area and trigger the corresponding early warning mechanism. In order to improve the credibility of the prediction results, the system will dynamically adjust the parameters of the support vector predictive controller so that the risk assessment can maintain high accuracy and robustness under different environmental conditions.

[0036] Embodiment 2: The absolute value of the difference in altitude between the sample ecological region and the target ecological region is less than the set altitude similarity threshold; the absolute value of the difference in humidity, the absolute value of the difference in temperature, and the absolute value of the difference in soil moisture content between the sample ecological region and the target ecological region within a set time range are respectively less than their respective corresponding similarity thresholds; at the same time, the sample ecological region and the target ecological region have the same vegetation type; the sample ecological region is an ecological region determined to have water pollution, soil pollution, and air pollution.

[0037] Specifically, the absolute value of the difference in altitude between the sample area and the target area is less than the set altitude similarity threshold. The setting of this condition stems from the significant impact of altitude on the ecological environment. Altitude directly affects key ecological variables such as air pressure, temperature, humidity and biodiversity. If the altitude difference between the sample area and the target area is too large, although other environmental parameters may be consistent, the dynamic evolution law of the ecosystem may be completely different. For example, the climate in high-altitude areas is usually colder, and the plant growth cycle and soil characteristics are quite different from those in low-altitude areas. Therefore, an altitude similarity threshold is set to ensure that the two areas have similar environmental pressure conditions, so that the pollution dynamics of the sample area can be more accurately used to predict changes in the target area. Secondly, this embodiment further stipulates that the absolute value of the difference in humidity, temperature and soil moisture content between the sample area and the target area within a set time range is less than their respective similarity thresholds. Humidity and temperature are important meteorological variables that affect the dynamics of the ecosystem. They are directly related to evaporation, precipitation distribution, plant transpiration and microbial activity. Soil moisture reflects the moisture conditions of the regional soil, which not only affects the growth and nutrient absorption of plant roots, but is also closely related to the migration and degradation of pollutants in the soil. For example, heavy metal contaminants in soil may migrate more easily in humid areas, while they may be more concentrated in the surface layer in arid areas. By setting similar thresholds for humidity, temperature, and soil moisture, the ecological conditions in the sample area are ensured to be consistent with those in the target area, thereby ensuring the comparability of pollution migration and diffusion patterns. The selection of these thresholds needs to be combined with the specific research objects and geographical characteristics of the ecosystem, and is usually optimized through historical monitoring data and field verification.

[0038] In addition, the sample area and the target area must have the same vegetation type, which is an important basis for the similarity of ecosystems. Vegetation type determines the ecological structure and function in the region, such as the photosynthesis rate of plants, carbon cycle efficiency and responsiveness to environmental pressure. Different vegetation types may also have significant differences in the absorption, degradation and fixation of pollutants. For example, coniferous forests and broad-leaved forests have different adsorption capacities for air pollutants, while grasslands and forests have different efficiencies in soil pollutant degradation. Therefore, by ensuring that the sample area and the target area have the same vegetation type, it can be ensured that the pollution dynamics of the sample area are applicable to the target area. Finally, this embodiment emphasizes that the sample ecological area is an ecological area with confirmed water pollution, soil pollution and air pollution. The design purpose of this condition is to ensure that the data of the sample area is sufficiently representative and of research value. The data of the polluted area often contains rich dynamic information, such as the migration pattern, cumulative effect and system recovery process of pollutants. This information provides a reliable basis for the training of the machine learning model, so that the model can extract complex dynamic laws from the pollution data and be used to predict the pollution risk of the target area. Through the definition and constraints of the above similarity conditions, Example 2 effectively solves the problem of the influence of the differences between ecosystems on the prediction accuracy of the model. This multi-parameter similarity screening mechanism not only improves the adaptability of the sample area data to the target area, but also enhances the generalization ability of the machine learning model, enabling the model to make reliable predictions in different but similar ecological environments. Compared with the prior art, the innovation of this embodiment in the area selection and data matching method is that it fully considers the multidimensional characteristics and complexity of the ecosystem, takes altitude, meteorological variables, soil moisture and vegetation type as key similarity indicators, and forms a comprehensive and scientific ecological similarity assessment framework.

[0039] Example 3: The air data includes: sulfur dioxide concentration, nitrogen oxide concentration, ozone concentration, PM2.5 concentration, temperature and humidity; the water quality data includes: dissolved oxygen content, chemical oxygen demand, biological oxygen demand, total phosphorus, total nitrogen, water pH value and turbidity; the soil data includes: soil moisture content, soil pH value, organic matter content, soil temperature, lead content, cadmium content, mercury content and salt concentration.

[0040] Specifically, the air data section includes sulfur dioxide concentration, nitrogen oxide concentration, ozone concentration, PM2.5 concentration, temperature and humidity. These indicators are the core components of atmospheric environmental monitoring and are also important factors reflecting the state of the ecosystem. Sulfur dioxide and nitrogen oxides are important pollutants emitted by industry and transportation. They not only directly affect air quality, but also participate in atmospheric chemical reactions to generate acid rain and secondary aerosols, causing extensive and far-reaching impacts on regional ecosystems. As a secondary pollutant, the concentration change of ozone reflects the intensity of photochemical reactions in the region and can indicate the complex dynamic characteristics of air pollution. PM2.5 is the main representative of atmospheric particulate matter, and its concentration is directly related to public health and the health of the ecosystem. Temperature and humidity, as basic meteorological variables, directly affect the distribution and migration patterns of atmospheric pollutants. For example, high temperature and low humidity usually intensify the generation of ozone, while increased humidity may lead to intensified secondary generation of PM2.5. The combination of these indicators provides a comprehensive multidimensional perspective for the monitoring and prediction of the atmospheric environment. Secondly, water quality data covers dissolved oxygen content, chemical oxygen demand, biological oxygen demand, total phosphorus, total nitrogen, water pH and turbidity. The selection of these indicators reflects a comprehensive assessment of the health status of the water ecosystem. Dissolved oxygen content is a direct representation of oxygen in the water body, reflecting the quality of the living environment of aquatic organisms; chemical oxygen demand and biological oxygen demand are important indicators for measuring the load of organic pollutants in water bodies, which can reveal the impact of pollutants on the self-purification capacity of water bodies. Total phosphorus and total nitrogen are the main causes of eutrophication of water bodies, and their concentration levels can indicate possible changes in the water ecosystem, such as algae outbreaks and changes in the structure of aquatic biological communities. The pH value and turbidity of water reflect the acid-base balance and transparency of the water body, respectively, and are important parameters for comprehensive assessment of water quality. Through the dynamic monitoring of these indicators, the system can identify the source, migration path and potential ecological impact of water pollution, thereby providing key data support for early warning and management of water quality risks. Finally, soil data include soil moisture content, soil pH value, organic matter content, soil temperature, lead content, cadmium content, mercury content and salt concentration. These indicators comprehensively cover the key elements of soil physical, chemical and biological properties. Soil moisture content directly affects the water absorption capacity of plant roots and the activity of soil microorganisms, which in turn affects the overall function of the soil ecosystem; soil pH determines the availability of nutrients and pollutants and is an important basic indicator for evaluating soil health. Organic matter content reflects the fertility and structural stability of the soil, and its changes may indicate soil degradation or ecosystem damage. Lead, cadmium and mercury are representative indicators of heavy metal pollution in soil, and changes in their concentrations directly threaten plant, microbial and human health, while salt concentration is an important parameter for measuring the degree of soil salinization and has a direct impact on plant growth and agricultural production. Soil temperature is closely related to soil microbial activity and chemical reaction rates, and is an auxiliary indicator for evaluating the dynamics of soil ecosystems.The joint monitoring of these indicators can comprehensively reveal the health status of the soil system and its possible pollution risks.

[0041] Embodiment 4: By using the following formula, cross-mapping based on the kernel function is performed on the sensor data vectors acquired by all sample sensor groups before the current time to obtain the mapping feature:

[0042] ;

[0043] in, is the mapping feature; Indicates The sample sensor group and The distance between the sample sensor groups; is the number of sample sensor groups; and All are integer subscript indices; For the The sample sensor group The sensor data vector acquired at the time; For the The sample sensor group The sensor data vector acquired at the time; is the Hilbert-Schmidt operator; each sensor data vector is composed of three sub-component vectors, namely: a moisture component vector corresponding to water quality data, an air component vector corresponding to air data, and a soil component vector corresponding to soil data.

[0044] Specifically, in the formula, the mapping feature The definition of is based on the physical distance between sample sensor groups and the similarity of environmental variables, and further introduces the Hilbert-Schmidt operator as an enhanced expression of the complex relationship between variables. Specifically, the physical distance between the sample sensor groups Characterizes the spatial correlation between ecological regions. In environmental systems, there is often a stronger dynamic coupling between adjacent regions, such as pollutant diffusion paths or similar climate conditions between regions. Therefore, by introducing physical distance, the formula can reflect the importance of spatial similarity in weight distribution, thereby ensuring that when calculating mapping features, data from similar areas contribute more, while data from distant areas have less impact. This design directly reflects the important role of spatial distribution in ecological environment monitoring. In addition, the formula utilizes the exponential decay characteristics of the kernel function and measures the similarity between data vectors of sample sensor groups based on Euclidean distance. Environmental data vector and The difference is expressed as the Euclidean distance, which not only reflects the absolute value change of the environmental variables, but also captures the subtle differences in the temporal dynamics between regions. The negative exponential form in the exponential function ensures that sample pairs with higher data similarity have greater weights, while naturally downgrading the weights of sample pairs with significant differences. This processing method is extremely important in ecological and environmental monitoring, because although the regions where the sample sensor groups are located may differ in large-scale features, the exponential decay can more sensitively capture those environmental dynamics that are highly similar in local features. For example, when the air quality indicators such as PM2.5 and nitrogen oxide concentrations of two sample areas are close, even if their geographical locations are far apart, the system can still identify the similarity of their data vectors through the formula and highlight their associations in the mapping features.

[0045] More importantly, the Hilbert-Schmidt operator is introduced into the formula , the role of this operator is to strengthen the description of complex nonlinear relationships between environmental variables. The dynamic changes of ecosystems are often not driven by a single variable, but by the coupling between multiple variables. For example, the concentration of nitrogen oxides in the air may work together with the organic matter content in the soil and the total phosphorus concentration in the water to affect the overall state of the regional ecosystem. Through the Hilbert-Schmidt operator, the interactions between these complex variables are effectively integrated into the mapping features, making The expressive power of is not only limited to the analysis of the similarity of a single variable, but also goes deep into the dynamic coupling relationship between multiple variables. Another key design in the formula is the number of sample sensor groups. Normalizes the overall mapping features. By weighting the number of samples, the formula ensures that the feature extraction process can adapt to sample data sets of different sizes without losing accuracy or sensitivity due to too many or too few samples. This is particularly important in large-scale ecological and environmental monitoring, because the spatial distribution of sensors may be uneven, and the formula The normalization process successfully solves this potential problem, enabling the system to maintain consistent feature extraction capabilities in diverse sample data.

[0046] Example 5: The state reconstruction equation of the dynamic system is constructed by the following formula:

[0047] ;

[0048] in, is the state change vector; is the divergence operator; is the Laplace operator; for The determinant of ; for traces; is the air pollution diffusion coefficient, , is the Boltzmann constant, is the temperature, is the air viscosity, is the short path of the sample ecoregion; is the water pollution diffusion coefficient, and its value range is arrive ; for arrive ; for The air component vector of the mean vector of all sensor data vectors acquired at the time; for The moisture component vector of the mean vector of all sensor data vectors acquired at the time; for The soil component vector of the mean vector of all sensor data vectors acquired at time.

[0049] Specifically, in the first part of the formula, the divergence and Laplace operators are combined to describe the diffusion behavior of the state change vector in space. It reflects the local change trend of the state of a certain area in the ecosystem. Positive divergence usually corresponds to the outflow of state, such as the diffusion of pollutants from high concentration areas, while negative divergence indicates the inflow of state, which may be the local accumulation of pollutants in a certain area. The Laplace operator related to this is The second-order spatial variation of the state is further quantified, which reflects the homogenization process of the state in the entire space. Through the combination of these operators, the system can dynamically simulate the complex interactions between variables in the ecological environment at different spatial points, such as the diffusion of air pollutants with the wind, the migration of water pollutants in the flow, and the distribution changes of soil pollutants at different depths. In order to make this spatial diffusion behavior adapt to the characteristics of different ecological environments, the characteristic matrix is ​​introduced into the formula This matrix is ​​derived from the high-dimensional features obtained by kernel function mapping, and contains the spatial correlation and dynamic feature information between the sample sensor groups. The state diffusion can adapt to the characteristic differences of different sample areas, thus reflecting higher flexibility and adaptability in the model. The second part of the formula simulates the dynamic migration characteristics of air, water and soil pollution in the ecological environment by introducing the pollution diffusion coefficient and the sensor mean vector. They are used to describe the diffusion process in air, water and soil respectively. Their physical meaning is to describe the diffusion capacity and rate of pollutants in different media. For example, the air pollution diffusion coefficient It is an expression based on the Boltzmann constant, temperature, air viscosity and short diameter, which fully reflects the influence of thermal motion of gas molecules on pollution diffusion. Under high temperature conditions, the thermal motion of molecules intensifies, resulting in an increase in the diffusion coefficient and an acceleration in the diffusion rate of pollutants. The diffusion coefficient of water pollution is and soil pollution diffusion coefficient The value range of directly reflects the difference in the diffusion ability of pollutants in liquid and solid media. These parameters enable the model to more accurately describe the migration patterns of pollutants in different environments.

[0050] In this part, the mean of the sensor data vector is divided into three sub-vectors corresponding to the air components , water content and soil weight These mean vectors are obtained by combining all sample sensor groups at time The collected data is calculated comprehensively and represents the overall environmental state of the entire system at a specific time point. By applying the Laplace operator to these mean vectors, the system can further capture the changing trend of the pollutant distribution in space. For example, the air component The Laplace operator can be used to simulate the diffusion of pollutant gases, and the water content The corresponding term can be used to describe the dynamic changes of chemical pollutant concentrations in water bodies. In addition, the soil component The Laplace operator can reflect the migration and accumulation of heavy metals or organic pollutants in the soil. These dynamic changes are multiplied by the corresponding pollution diffusion coefficient and are expressed by the weight term in the formula Adjustment is made, thus reflecting the regulatory effect of the system's internal characteristic matrix on the external driving effect. is a key design in the formula, which is expressed by the matrix The ratio of the determinant and the trace value of , which links the sample characteristics to the pollutant diffusion process. It is a measure of the global characteristics of the matrix, indicating the overall influence of high-dimensional features; is the sum of the matrix eigenvalues, indicating the cumulative effect of local characteristics. Through the ratio of the determinant to the trace value, the formula can dynamically adjust the contribution of the pollution diffusion driving term in the system, thereby reflecting the global and local impact of sample characteristics on the dynamics of the entire ecological environment.

[0051] Example 6: Construct a Lyapunov function based on a support vector machine using the following formula:

[0052] ;

[0053] in, is the state change vector No. Quantity; is the curl operator; is the tensor product; is the Lyapunov function; is the L1 norm; Transpose operation for vectors or matrices.

[0054] Specifically, the Lyapunov function The first item is the global characteristic expression of the formula, which quantifies the state change vector through a quadratic function In the feature matrix The overall behavior in space. The characteristic matrix Generated by the kernel mapping of the support vector machine, it carries the dynamic characteristics and spatial correlation information of the sample ecological region. The state change vector is projected into a high-dimensional space and its global amplitude is quantified. Here, the positive definite property of the quadratic form ensures that this term is always non-negative, and its numerical value reflects the overall deviation of the state vector. For example, when the air, water quality, and soil pollution of an ecosystem change dramatically, the state vector The amplitude of will increase significantly, causing the value of this term to rise, indicating that the system deviates from the equilibrium state. When approaching the equilibrium point, the state vector amplitude tends to zero, and the quadratic value also decreases, indicating that the dynamics of the system gradually stabilizes. The second term The focus is on describing the local variation characteristics of the ecosystem. Here, the determinant of the Laplace operator is It describes the local second-order changes of the state vector components. Its physical meaning is to quantify the change amplitude of the variable in the local space. For example, when the dissolved oxygen content of water quality data drops rapidly in a certain area, the value of the Laplace operator will reflect this mutation, thus reflecting the local impact of the pollution event in the formula. A dynamic attenuation mechanism is introduced, so that the impact of this local change on the function value decreases as the amplitude of the state vector increases. Its role is to ensure that when the global change of the system dominates, the local characteristics will not have too much impact on the overall stability assessment, thereby achieving a balance between local and global dynamics.

[0055] The third term of the formula It is further extended to higher-order coupling relationships between variables. and divergence operator The local rotation and divergence of the state vector are respectively characterized, both of which have clear physical meanings in the ecological environment. For example, the curl can be used to describe the local dynamics of atmospheric circulation or water turbulence, while the divergence reflects the diffusion trend of pollutants from high concentration areas to low concentration areas. This operation combines the curl and divergence to form a comprehensive representation of the dynamic distribution of the state variables. The combination of the matrix determinant and the trace value further regulates the influence of high-order interactions between variables, making it adaptable to the dynamic characteristics of different ecological environments. In particular, the second-order time derivative in the matrix element product term and the first-order time derivative Characterizes the dynamic trend and rate of state change. For example, the second-order derivative of total phosphorus concentration in water quality data may reflect the acceleration of pollutant input or removal process, while the first-order derivative of PM2.5 concentration in the air reflects its diffusion rate. By combining these derivative terms with Laplace operator and divergence operator, the formula can capture the dynamic characteristics of pollutant migration in air, water and soil, and reveal the complex coupling relationship between variables, such as the linkage effect between air pollution and water pollution. The weight factor in the formula Through the feature matrix The determinant of adjusts the effect of the interaction between variables on the stability of the system. This design is based on the assumption that when the sample feature matrix When the determinant is large, the dynamic correlation between samples is stronger, and the contribution of high-order coupling between variables to the overall dynamics of the system should also increase accordingly; conversely, when the determinant is small, the coupling between variables has a weaker impact on the system.

[0056] Example 7: The entropy evolution function is obtained by performing entropy dynamic evolution analysis on the value of the Lyapunov function using the following formula:

[0057] ;

[0058] in, is the entropy evolution function.

[0059] Specifically, the core idea of ​​the entropy evolution function is to use the Lyapunov function The time evolution process of is integrated and analyzed to extract the stability information of the system at different time points. The first part of the formula quantifies the square value of the gradient of the Lyapunov function by processing the time integral term. The cumulative effect over time. This squared gradient term reflects the rate of change of the Lyapunov function and can reveal the trend and intensity of the system state deviating from the stable point. When the squared gradient is large, it means that the rate of change of the system state is fast and may be moving away from its stable state; conversely, when the squared gradient value is small, the system changes tend to be stable and may be close to stability. The normalization factor in the formula The squared gradient value is adjusted to have consistent dimensions and scales under different feature matrix conditions. This design ensures the robustness and generalization ability of the entropy evolution function when the characteristics of the sample ecological region change. For example, when the correlation between the sample sensor groups is strong, the feature matrix The determinant of When the value of the normalization factor is large, it can weaken the absolute value of the squared gradient, thereby avoiding its excessive impact on the entropy value. When the sample correlation is weak, the normalization factor will correspondingly amplify the squared gradient value to ensure the sensitivity of the entropy evolution function to system changes.

[0060] The product term in the formula The second-order time derivative of the state change component is introduced to reflect the acceleration effect in the dynamic evolution of the system. The second-order derivative describes the acceleration or deceleration trend of the state variable over time. In the ecological environment, this change may correspond to key dynamic characteristics such as the acceleration of pollutant diffusion and the deceleration of environmental recovery. For example, when the diffusion of air pollutant concentration accelerates, the second-order derivative is positive, and the attenuation of the exponential term makes the entropy value more sensitive to this change, which can promptly reflect the increase in system complexity; when the system change decelerates, the effect of the exponential term weakens, indicating that the system complexity decreases. Matrix determinant terms It is the key part of the entropy evolution formula, which captures the dynamic coupling relationship of the system in multidimensional state by jointly analyzing the curl, divergence and Laplace operator of the Lyapunov function. It reflects the rotation of the Lyapunov function in the local area, which is important in describing the circulation or cycle process in the ecosystem (such as water cycle or atmospheric circulation); the divergence term It is used to quantify the diffusion behavior of the Lyapunov function and reveal the divergence or contraction trend of the system state in space; the Laplace operator It further captures the smoothness and degree of change of the system state in spatial distribution. The combined effect of these operators reflects the balance between the global dynamics and local characteristics of the system through the matrix determinant, which can deeply analyze the complexity and stability of the system. The last part of the time integral term also includes a tensor product , which describes the high-order interactions of the Lyapunov function on each state component. This tensor product term reflects the coupling relationship between the state components and their impact on the stability of the system. For example, in an ecosystem affected by multiple pollutants at the same time, this term can capture the complex interactions between pollutants and their combined impact on the stability of the overall system. Finally, the logarithmic term in the formula It is a normalized expression of the global behavior of the Lyapunov function, quantifying its overall contribution to the evolution of entropy. Through logarithmic transformation, this term enhances the ability to characterize nonlinear changes in the system while avoiding the numerical overflow problem of the function value in extreme cases.

[0061] Example 8: Construct an optimal support vector predictive controller using the following formula:

[0062] ;

[0063] ;

[0064] ;

[0065] in, is the objective function value of the support vector; by minimizing the objective function, the discriminant correction value is obtained ; Indicates the constraints that are satisfied; is the target data vector, and also serves as the input vector; is the predicted judgment value.

[0066] Specifically, the core goal of the support vector predictive controller is to minimize the objective function , optimize the key parameters of the prediction model , enabling the system to generate accurate predictions based on current and historical environmental conditions In the formula, the first part of the objective function is The dynamic characteristics of the entropy evolution function and the Lyapunov function are combined. It quantifies the temporal evolution trend of ecosystem complexity, and its cumulative integral reflects the stability and complexity changes of the system over the entire time period. Incorporating the objective function allows the system to more fully evaluate the overall dynamics of the environmental state, ensuring that the output of the predictive controller captures the global characteristics of the ecological environment. The addition of further enhances the objective function's focus on system stability. As an indicator of ecosystem stability, the Lyapunov function can quantify the state change vector The degree of deviation from the equilibrium state. It is related to the characteristic matrix The multiplication of introduces the high-dimensional feature map of the support vector machine into the objective function, giving the controller stronger nonlinear dynamic analysis capabilities. For example, when the environmental state deviates from the stable point, the value of the Lyapunov function will increase significantly, and the value of the objective function will increase accordingly, thereby guiding the controller to optimize the parameters , adjust the prediction model to enhance the control over system stability.

[0067] The constraint part provides the necessary boundaries and restrictions for the optimization of the objective function. First, the constraint This ensures that the dynamic evolution speed of the system's entropy value can always meet certain growth requirements. The design of this constraint condition is based on the actual characteristics of state changes in the ecosystem. For example, the pollution diffusion process may cause a sharp increase in the complexity of the environmental system. At this time, the dynamic change rate of the entropy value needs to be fast enough to accurately reflect the changing trend of the environment. In addition, the time derivative of the Lyapunov function included in the condition This further constrains the stability of the system and ensures that the predictive controller can maintain stable performance when dealing with complex dynamics. The constraint also sets the parameter Upper limit of This limitation reflects the actual requirement of the system for the parameter range to avoid over-adjustment or numerical overflow during the optimization process. The controller avoids prediction inaccuracy or numerical instability caused by excessive parameter values ​​while ensuring flexibility. The second part of the formula Defined prediction value , whose main function is to measure the controller output result and the target data vector Here, the target data vector It is the current environmental state input of the system and also the main basis for the optimization of the predictive controller. Expressed as a negative exponential function, the system optimizes the parameters When the difference between the predicted value and the target data vector is small, the value of the exponential term tends to be the largest, indicating that the prediction accuracy of the controller is high; when the deviation is large, the exponential term decays rapidly, indicating that the controller needs to adjust the parameters To optimize the prediction performance. The mean term in the formula By integrating the state data of all sample sensor groups at the current time, comprehensive input information is provided to the controller. This mean calculation not only smoothes the random fluctuations of sensor data, but also enhances the predictive controller's ability to adapt to global features. For example, when a sensor group's data deviates due to an abnormal event (such as sudden pollution), the introduction of the mean term can reduce the negative impact of this deviation on the controller and ensure the robustness of the prediction model.

[0068] Example 9: If the predicted judgment value exceeds the set threshold, an ecological environment warning is issued.

[0069] Specifically, in this embodiment, the prediction judgment value It is generated by the support vector predictive controller and reflects the degree of deviation of the current ecosystem state from the historical monitoring data and the dynamic model. The size of the prediction value depends on the target data vector The degree of difference between the predicted judgment value and the model prediction output. When the predicted judgment value is small, it means that the ecological state of the target area is consistent with the stability expectation of the model, and the system is in a stable state; when the predicted judgment value exceeds the set threshold, it indicates that the ecological environment of the target area may have significant dynamic changes or potential risks. The setting of the threshold is the key to Example 9, and it needs to be scientifically selected according to the actual ecological environment characteristics and risk tolerance. Generally, the size of the threshold can be determined by historical data analysis, expert experience or based on statistical methods. For example, in air pollution monitoring, a threshold can be set according to the predicted judgment value of the highest pollution event in history to ensure that when the predicted judgment value approaches or exceeds this threshold, the system can respond in time and issue an early warning. In addition, the dynamic adjustment ability of the threshold is also very important. For example, during extreme weather or environmental emergencies (such as industrial accidents), the threshold can be appropriately lowered to increase the sensitivity of the system, so as to detect abnormal situations earlier. Once the predicted judgment value exceeds the set threshold, the system will immediately trigger the early warning mechanism. The form of early warning can be diversified, including displaying alarm information through a visual interface, sending notifications to relevant management departments, starting on-site monitoring or control equipment, etc. For example, when the system detects that the water pollution prediction judgment value of the target area exceeds the threshold, it can send an early warning signal to the local environmental management department, together with the prediction analysis results and possible sources of pollution. This real-time early warning can buy valuable response time for managers and effectively reduce the losses that may be caused by environmental risks. Another advantage of Example 9 is its flexibility and adaptability. Through deep integration with the support vector predictive controller, the system can automatically adjust the parameters of the prediction model according to the dynamic changes of the ecological environment, thereby optimizing the calculation accuracy of the judgment value. For example, when the monitoring data of the target area fluctuates violently due to an emergency, the system can update the feature matrix by retraining the support vector machine model. , so that the predicted judgment value reflects the actual risk more accurately. In addition, the system can also combine historical warning records and response results to gradually optimize the threshold setting method and improve the reliability and effectiveness of the warning mechanism.

[0070] The present invention is described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. The ecological environment dynamic monitoring system based on machine learning is characterized by: The system includes: a sensor monitoring part, a feature extraction and mapping part, a support vector analysis and decision-making part and a prediction risk assessment part; the sensor monitoring part includes a sample ecological area sensor group and a target ecological area sensor group; the sample ecological area sensor group includes a plurality of identical sample sensor groups, each of which acquires air data, water quality data and soil data of the sample ecological area in real time to form a sensor data vector; the target ecological area sensor group includes only one target sensor group that is identical to the sample sensor group, which acquires air data, water quality data and soil data of the sample ecological area in real time to form a target data vector; the feature extraction and mapping part is used to perform sensor data vectors acquired by all sample sensor groups before the current time. Based on the cross-mapping of kernel functions, mapping features are obtained; based on the mapping features, a dynamic system state reconstruction equation is constructed to simulate the changes in the target ecological area, and the dynamic system state reconstruction equation is solved to obtain a state change vector; the support vector analysis and decision-making part is used to take the state change vector as an input value, construct a Lyapunov function based on a support vector machine, and perform an entropy value dynamic evolution analysis on the value of the Lyapunov function to obtain an entropy evolution function; combining the state change vector and the entropy evolution function, and then constructing an optimal support vector prediction controller based on the entropy evolution function; the prediction risk assessment part is used to take the target data vector as an input vector, input it into the optimal support vector prediction controller, generate a prediction judgment value, and judge whether an ecological environment early warning is needed based on the prediction judgment value; The following formula is used to perform cross-mapping based on the kernel function on the sensor data vectors acquired by all sample sensor groups before the current time to obtain the mapping features: ； in, is the mapping feature; Indicates The sample sensor group and The distance between the sample sensor groups; is the number of sample sensor groups; and All are integer subscript indices; For the The sample sensor group The sensor data vector acquired at the time; For the The sample sensor group The sensor data vector acquired at the time; is the Hilbert-Schmidt operator; each sensor data vector is composed of three sub-component vectors, namely: a moisture component vector corresponding to water quality data, an air component vector corresponding to air data, and a soil component vector corresponding to soil data; The state reconstruction equation of the dynamic system is constructed through the following formula: ； in, is the state change vector; is the divergence operator; is the Laplace operator; for The determinant of ; for traces; is the air pollution diffusion coefficient, , is the Boltzmann constant, is the temperature, is the air viscosity, is the short path of the sample ecoregion; is the water pollution diffusion coefficient, and its value range is arrive ; for arrive ; for The air component vector of the mean vector of all sensor data vectors acquired at the time; for The moisture component vector of the mean vector of all sensor data vectors acquired at the time; for The soil component vector of the mean vector of all sensor data vectors acquired at time.

2. The ecological environment dynamic monitoring system based on machine learning as claimed in claim 1, characterized in that: The absolute value of the difference in altitude between the sample ecological region and the target ecological region is less than the set altitude similarity threshold; the absolute value of the difference in humidity, the absolute value of the difference in temperature, and the absolute value of the difference in soil moisture content between the sample ecological region and the target ecological region within a set time range are respectively less than their respective corresponding similarity thresholds; at the same time, the sample ecological region and the target ecological region have the same vegetation type; the sample ecological region is an ecological region that is determined to have water pollution, soil pollution, and air pollution.

3. The ecological environment dynamic monitoring system based on machine learning as claimed in claim 2, characterized in that: The air data include: sulfur dioxide concentration, nitrogen oxide concentration, ozone concentration, PM2.5 concentration, temperature and humidity; the water quality data include: dissolved oxygen content, chemical oxygen demand, biological oxygen demand, total phosphorus, total nitrogen, water pH value and turbidity; the soil data include: soil moisture content, soil pH value, organic matter content, soil temperature, lead content, cadmium content, mercury content and salt concentration.

4. The ecological environment dynamic monitoring system based on machine learning as claimed in claim 3, characterized in that: The Lyapunov function based on support vector machine is constructed by the following formula: ； in, is the state change vector No. Quantity; is the curl operator; is the tensor product; is the Lyapunov function; is the L1 norm; Transpose operation for vectors or matrices.

5. The ecological environment dynamic monitoring system based on machine learning as claimed in claim 4, characterized in that: The entropy evolution function is obtained by analyzing the dynamic evolution of the entropy value of the Lyapunov function through the following formula: ； in, is the entropy evolution function.

6. The ecological environment dynamic monitoring system based on machine learning as claimed in claim 5, characterized in that: The optimal support vector predictive controller is constructed by the following formula: ； ； ； in, is the objective function value of the support vector; by minimizing the objective function, the discriminant correction value is obtained ; Indicates the constraints that are satisfied; is the target data vector, and also serves as the input vector; is the predicted judgment value.

7. The ecological environment dynamic monitoring system based on machine learning as claimed in claim 6, characterized in that: If the predicted judgment value exceeds the set threshold, an ecological environment warning will be issued.

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