Ecological environment real-time dynamic monitoring system based on machine learning

Abstract

The invention discloses an ecological environment real-time dynamic monitoring system based on machine learning, and belongs to the technical field of machine learning. Comprising a multi-dimensional ecological signal capture module, a dynamic causal topology construction module, a space-time correlation deduction module, a quantum annealing environment state prediction module, an adversarial generative network dynamic correction module, a dynamic adaptive sensing network module and a chaos early warning module. The space attenuation factor and the causal edge weight are combined to construct the space-time causal feature representation, the predicted space-time resolution is significantly improved, especially in a complex terrain or a multi-source pollution scene, the local anomaly of the environment change can be more accurately captured, and the high-dimensional nonlinear relation of the environment state is simulated by constructing the quantum energy function, so that the prediction accuracy is improved. The influence of the causal relationship on the system energy is simulated through the spin operator and the coupling coefficient, and the method has double advantages in prediction precision and calculation efficiency, and is suitable for the quick response demand of sudden environmental events.

Description

Technical Field

[0001] The present invention belongs to the field of machine learning technology, and specifically refers to a real-time dynamic monitoring system for the ecological environment based on machine learning. Background Art

[0002] With the rapid development of industrialization and urbanization, ecological and environmental problems are becoming increasingly prominent. Traditional environmental monitoring systems have problems such as low data collection frequency, simple analysis methods, and weak early warning capabilities, making it difficult to meet the needs of modern ecological and environmental management.

[0003] However, the existing real-time dynamic monitoring system of the ecological environment still has certain defects. The existing causal analysis methods are difficult to capture the nonlinear and time-varying relationships in the dynamic environment, resulting in poor stability of the causal network. Spatiotemporal modeling often uses independent spatial or temporal feature extraction, resulting in a lack of spatiotemporal consistency in the prediction results. The prediction model is prone to fall into local optimality when processing high-dimensional nonlinear environmental data, and the computational efficiency is low. The data correction method relies on prior assumptions for the treatment of missing values ​​and noise, and is difficult to adapt to the nonlinear characteristics of the dynamic environment. For this reason, a real-time dynamic monitoring system of the ecological environment based on machine learning is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a real-time dynamic monitoring system for the ecological environment based on machine learning to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a real-time dynamic monitoring system for the ecological environment based on machine learning, comprising a multidimensional ecological signal capture module, a dynamic causal topology construction module, a spatiotemporal correlation deduction module, a quantum annealing environmental state prediction module, a generative adversarial network dynamic correction module, a dynamic adaptive perception network module, and a chaos early warning module;

[0006] The multi-dimensional ecological signal capture module is used for distributed heterogeneous sensor networks to collect multi-dimensional environmental data;

[0007] The dynamic causal topology construction module performs dynamic causal relationship modeling based on the multidimensional environmental data of the multidimensional ecological signal capture module;

[0008] The spatiotemporal correlation deduction module performs spatiotemporal feature joint modeling based on the causal network of the dynamic causal topology construction module and the multidimensional environmental data of the multidimensional ecological signal capture module;

[0009] The quantum annealing environment state prediction module performs high-dimensional nonlinear prediction based on the spatiotemporal characteristics of the spatiotemporal correlation deduction module and the causal network of the dynamic causal topology construction module to generate an environment state prediction result;

[0010] The adversarial generative network dynamic correction module generates missing data and performs noise correction based on the original data of the multidimensional ecological signal capture module and the prediction error of the quantum annealing environment state prediction module, and feeds back to the dynamic causal topology construction module;

[0011] The chaos warning module performs mutation event identification and warning based on the prediction results of the quantum annealing environment state prediction module and the spatiotemporal characteristics of the spatiotemporal correlation deduction module;

[0012] The dynamic adaptive perception network module dynamically adjusts the sensor layout and sampling frequency according to the prediction results of the quantum annealing environment state prediction module and the warning signal of the chaos warning.

[0013] Preferably, the multi-dimensional ecological signal capture module is used for distributed heterogeneous sensor networks to collect multi-dimensional environmental data; through multiple heterogeneous sensors, grids are divided according to the environmental characteristics of the monitoring area, the sampling frequency is set according to the sensor characteristics, global time synchronization of the sensor network is achieved through GPS, metadata is added to each data, and a hierarchical transmission network is constructed by combining wired and wireless technologies for data transmission. Through the central control station function of the distributed data acquisition system, heterogeneous sensor data is fused, sensor data at different locations are mapped to a unified spatial coordinate system, and sensor data of different frequencies are aligned through interpolation.

[0014] Preferably, the dynamic causal topology building module, the dynamic causal strength evolution step: obtaining the time series data X=[x1(t),x2(t),...,x i (t)],x i (t) represents the observed value of the i-th ecological variable at time t. The dynamic causal strength evolves over time t through the causal influence of node i on node j. The dynamic causal strength evolution is carried out by combining the differential causal effect and the exponential decay factor. The realization formula is:

[0015]

[0016] In the formula, C ij (t) represents the dynamic causal strength, represents the instantaneous causal effect of node i on node j, e -λΔt represents the exponential decay factor, Δt=tt ls Indicates the current time t and the most recent observation time t ls The interval, η represents the weight of the external regulatory factor, u(t) represents the regulation of the exogenous variable on the dynamic causal intensity, F j Represents the state function of node j, partial derivative Represents x i The unit change of F j impact.

[0017] Preferably, the dynamic causal topology construction module, the dynamic causal key construction and update step: according to the dynamic causal strength, the edge weight update of the dynamic causal network is constructed, and the implementation formula is:

[0018]

[0019] In the formula, W ij (t+1) represents the edge weight from node i to j at time t+1, α represents the smoothing coefficient, and the smoothing coefficient is combined with the historical weight W ij (t) and the current dynamic causal strength, W ij (t) represents the edge weight of the spatiotemporal causal network, represents the normalized update of the current dynamic causal strength, ∑ k |C ij (t)| represents the normalization factor, k represents traversing all variables related to node i, |C ij (t)| means taking the absolute value to avoid negative values ​​interfering with normalization.

[0020] Preferably, the spatiotemporal correlation deduction module and the spatiotemporal feature joint modeling implementation steps are: obtaining the multidimensional environmental data x of the multidimensional ecological signal capture module i (t) and the edge weights of the dynamic causal network to generate spatiotemporal causal feature representation, and the implementation formula is:

[0021]

[0022] In the formula, S i (t) represents the spatiotemporal causal feature representation, represents the spatial attenuation factor, d ij Represents the physical distance between nodes i and j.

[0023] Preferably, the quantum annealing environment state prediction module, the quantum annealing energy function construction implementation step: obtaining the spatiotemporal causal feature representation S i (t) and the edge weights W of the spatiotemporal causal network ij (t), combined with the construction of quantum annealing energy function, the high-dimensional nonlinear relationship of the environmental state is described, and the implementation formula is:

[0024]

[0025] In the formula, E QA represents the quantum annealing energy function, γ represents the longitudinal magnetic field intensity parameter, and controls the contribution weight of the spatiotemporal characteristics to the energy. represents the spin operator, represents the state of node i, η ij represents the coupling coefficient between nodes i and j, reflecting the strength of the causal relationship, represents the spin interaction term between nodes i and j, simulating the effect of causality on the system energy, Δ represents the transverse magnetic field intensity parameter, represents the x-component of the spin operator.

[0026] Preferably, the quantum annealing environment state prediction module has an environment state prediction implementation step: according to the quantum annealing energy function, through the annealing process evolution, generating the environment state prediction result at time t+1, and the implementation formula is:

[0027]

[0028] In the formula, x i (t+1) represents the environmental prediction result, It means finding x that minimizes the objective function i , Δ(t) represents the transverse magnetic field intensity parameter at time t.

[0029] Preferably, the adversarial generative network dynamic correction module generates missing data and corrects noise based on the original data of the multidimensional ecological signal capture module and the prediction error of the quantum annealing environment state prediction module, and feeds back to the dynamic causal topology construction module; obtains the original environmental data from the multidimensional ecological signal capture module, and simultaneously obtains the prediction results and corresponding prediction errors of the quantum annealing environment state prediction module, aligns the original data and the prediction error according to the time dimension to form a joint input sample, and marks the missing areas and noise areas in the data;

[0030] Preferably, a generative adversarial network is constructed, and local segments of the original data and the prediction error are used as conditional information to generate corrected data segments. Through the conditional generative adversarial network, the prediction error is used as the conditional input to guide the generator to learn how to perform corrections in combination with dynamic causal relationships; the discriminator compares the original data segments with the corrected data segments output by the generator to determine whether the input data is real data, and a multi-layer perceptron convolutional neural network (CNN) is used to extract the spatiotemporal features of the data and perform binary classification;

[0031] For discriminator training, the generator parameters are fixed, and real data and generated data fragments are input. By minimizing the discriminator's binary classification loss, its ability to distinguish between real and generated data is improved. For generator training, the discriminator parameters are fixed, and the original data and prediction error are input. The generator performance is optimized by maximizing the generator's adversarial loss and reconstruction loss.

[0032] Dynamically adjust the generator's loss function weight according to the magnitude of the prediction error. If the prediction error is large, increase the adversarial loss weight to make the generator pay more attention to global consistency. If the prediction error is small, increase the reconstruction loss weight to ensure the accuracy of local detail correction.

[0033] Preferably, the missing areas of the original data are input into the trained generator, and the prediction error is used as a condition to generate the filled data, the noise area is smoothed, the dynamic causal relationship learned by the generator is used to suppress abnormal fluctuations, the generated data is statistically tested, and the correction effect is verified through a sliding window. If the error does not converge, the generator parameters are readjusted, and the corrected data is combined with the original data to form a new data set. A higher confidence weight is given to the missing area and the noise area, which is used preferentially for causal inference. The corrected data is input into the dynamic causal topology construction module, and the causal weight is recalculated. The corrected data enhances the robustness of causal inference and reduces false causal relationships caused by missing data or noise.

[0034] Preferably, the chaos early warning module performs mutation event identification and early warning based on the prediction results of the quantum annealing environment state prediction module and the spatiotemporal features of the spatiotemporal correlation deduction module; obtains the prediction results from the quantum annealing environment state prediction module and the spatiotemporal features from the spatiotemporal correlation deduction module, aligns the prediction results with the spatiotemporal features in the time dimension and the space dimension, unifies the timestamps, ensures that the time window of the prediction value is consistent with the time window of the spatiotemporal features, maps the prediction value and the spatiotemporal features to a unified spatial grid, extracts key dynamic indicators from the prediction results, and extracts abnormal patterns from the spatiotemporal features;

[0035] Specifically: Using the dynamic relationship between prediction results and spatiotemporal characteristics, we construct a chaotic characteristic index of the mutation event. By comparing the deviation between the predicted value and the actual observed value, we determine whether there is a nonlinear mutation and calculate the consistency between the predicted value and the spatiotemporal characteristics.

[0036] The threshold of mutation events is set according to the distribution of historical data. The static threshold is based on the mean and standard deviation of long-term observation data. The dynamic threshold is combined with the dynamic changes of spatiotemporal characteristics to identify mutation events that exceed the threshold through anomaly detection algorithms. The prediction error, spatiotemporal mismatch, dynamic indicators and other features are input into the classifier to train the mutation event recognition model. The early warning rules are set according to the severity level of the mutation event. The prediction results and the dynamic changes of spatiotemporal characteristics are continuously monitored. The probability of the current mutation event is calculated through a sliding window. If the probability exceeds the preset threshold, an early warning is immediately triggered and an event report is generated.

[0037] Preferably, the dynamic adaptive perception network module dynamically adjusts the sensor layout and sampling frequency according to the prediction results of the quantum annealing environment state prediction module and the warning signal of the chaos warning; obtains the predicted value of the environment state from the quantum annealing environment state prediction module, obtains the warning signal of the mutation event from the chaos warning module, maps the prediction result and the warning signal to a unified time-space coordinate system to form a dynamic hot spot area, divides the area priority according to the severity of the warning signal, determines the urgency of sensor adjustment, transforms the sensor layout adjustment problem into a combinatorial optimization problem, optimizes the position of the sensor nodes through the quantum annealing algorithm, and increases the sensor density in the mutation area indicated by the chaos warning signal;

[0038] In areas where the predicted values ​​show rapid changes in environmental parameters, the sampling frequency is increased, and in areas where the predicted values ​​are stable, the sampling frequency is reduced. When the chaos warning module detects a sudden anomaly, the emergency sampling mode is immediately activated, and the edge computing module is deployed at the sensor node. The sampling rate is dynamically adjusted according to real-time data fluctuations, and the warning signal of the mutation event is fed back to the quantum annealing environmental state prediction module. The parameters of the prediction model are optimized, and the improved prediction results are fed back to the chaos warning module to improve the accuracy of mutation event identification.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. This invention jointly models a dynamic causal network with multidimensional environmental data, combines spatial attenuation factors with causal edge weights, and constructs a spatiotemporal causal feature representation. This resolves the contradiction between spatial heterogeneity and temporal non-stationarity, significantly improving the spatiotemporal resolution of predictions. This allows for more accurate capture of local anomalies in environmental changes, particularly in complex terrain or multi-source pollution scenarios.

[0041] 2. This invention simulates the high-dimensional nonlinear relationship of environmental states by constructing a quantum energy function, and simulates the influence of causal relationships on system energy through spin operators and coupling coefficients. It has the dual advantages of prediction accuracy and computational efficiency, and is particularly suitable for the rapid response requirements of sudden environmental events.

[0042] 3. This invention uses a conditional generative adversarial network (CGN) to generate correction data segments using prediction errors as conditional input. By dynamically adjusting the weights of the generator loss function, it balances global consistency with local detail correction, significantly improving the reliability of the correction data. It also generates reasonable replacement values ​​based on dynamic causal relationships, avoiding causal inference biases caused by data holes and effectively suppressing abnormal fluctuations. Furthermore, through a confidence weighting strategy, it improves the effectiveness of correction data in causal modeling.

[0043] 4. The present invention optimizes the sensor layout through quantum annealing, combines edge computing to dynamically adjust the sampling frequency, and encrypts the sensor density around the fire source in real time. At the same time, it shuts down redundant nodes far away from the area, significantly reducing overall energy consumption, dynamically matching the needs of environmental changes, and extending the use of sensors while ensuring monitoring accuracy. The feedback mechanism optimizes the prediction model and early warning rules. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of the structure of the real-time dynamic monitoring system of the ecological environment based on machine learning of the present invention;

[0045] Figure 2 This is the operating process of the real-time dynamic monitoring system of the ecological environment based on machine learning of the present invention Figure 1 ;

[0046] Figure 3 This is the operating process of the real-time dynamic monitoring system of the ecological environment based on machine learning of the present invention Figure 2 ;

[0047] Figure 4 This is the operating process of the real-time dynamic monitoring system of the ecological environment based on machine learning of the present invention Figure 3 . DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0049] Example

[0050] See also Figures 1-4 As shown, the present invention provides a technical solution: including a multi-dimensional ecological signal capture module, a dynamic causal topology construction module, a spatiotemporal correlation deduction module, a quantum annealing environment state prediction module, a dynamic correction module of the adversarial generative network, a dynamic adaptive perception network module and a chaos early warning module;

[0051] The multi-dimensional ecological signal capture module is used for distributed heterogeneous sensor networks to collect multi-dimensional environmental data;

[0052] The dynamic causal topology construction module performs dynamic causal relationship modeling based on the multidimensional environmental data of the multidimensional ecological signal capture module;

[0053] The spatiotemporal correlation deduction module performs spatiotemporal feature joint modeling based on the causal network of the dynamic causal topology construction module and the multidimensional environmental data of the multidimensional ecological signal capture module;

[0054] The quantum annealing environment state prediction module performs high-dimensional nonlinear prediction based on the spatiotemporal characteristics of the spatiotemporal correlation deduction module and the causal network of the dynamic causal topology construction module to generate an environment state prediction result;

[0055] The adversarial generative network dynamic correction module generates missing data and performs noise correction based on the original data of the multidimensional ecological signal capture module and the prediction error of the quantum annealing environment state prediction module, and feeds back to the dynamic causal topology construction module;

[0056] The chaos warning module performs mutation event identification and warning based on the prediction results of the quantum annealing environment state prediction module and the spatiotemporal characteristics of the spatiotemporal correlation deduction module;

[0057] The dynamic adaptive perception network module dynamically adjusts the sensor layout and sampling frequency according to the prediction results of the quantum annealing environment state prediction module and the warning signal of the chaos warning.

[0058] Preferably, the multi-dimensional ecological signal capture module is used for distributed heterogeneous sensor networks to collect multi-dimensional environmental data; through multiple heterogeneous sensors, grids are divided according to the environmental characteristics of the monitoring area, the sampling frequency is set according to the sensor characteristics, global time synchronization of the sensor network is achieved through GPS, metadata is added to each data, and a hierarchical transmission network is constructed by combining wired and wireless technologies for data transmission. Through the central control station function of the distributed data acquisition system, heterogeneous sensor data is fused, sensor data at different locations are mapped to a unified spatial coordinate system, and sensor data of different frequencies are aligned through interpolation.

[0059] In this embodiment, specifically, the dynamic causal topology construction module, the dynamic causal strength evolution step: obtaining the time series data X=[x1(t),x2(t),...,x i (t)],x i (t) represents the observed value of the i-th ecological variable at time t;

[0060] In this embodiment, specifically, the dynamic causal strength is evolved by combining the differential causal effect and the exponential decay factor through the evolution of the causal influence of node i on node j over time t. The implementation formula is:

[0061]

[0062] In the formula, C ij (t) represents the dynamic causal strength, represents the instantaneous causal effect of node i on node j, e -λΔt represents the exponential decay factor, Δt=ttls Indicates the current time t and the most recent observation time t ls The interval, η represents the weight of the external regulatory factor, u(t) represents the regulation of the exogenous variable on the dynamic causal intensity, F j Represents the state function of node j, partial derivative Represents x i The unit change of F j impact.

[0063] In this embodiment, specifically, the dynamic causal topology construction module, the dynamic causal key construction and update step: construct the edge weight update of the dynamic causal network according to the dynamic causal strength, and the implementation formula is:

[0064]

[0065] In the formula, W ij (t+1) represents the edge weight from node i to j at time t+1, α represents the smoothing coefficient, and the smoothing coefficient is combined with the historical weight W ij (t) and the current dynamic causal strength, W ij (t) represents the edge weight of the spatiotemporal causal network, represents the normalized update of the current dynamic causal strength, ∑ k |C ij (t)| represents the normalization factor, k represents traversing all variables related to node i, |C ij (t)| means taking the absolute value to avoid negative values ​​interfering with normalization.

[0066] In this embodiment, specifically, the spatiotemporal correlation deduction module and the spatiotemporal feature joint modeling implementation steps are: obtaining the multidimensional environmental data x of the multidimensional ecological signal capture module i (t) and the edge weights of the dynamic causal network to generate spatiotemporal causal feature representation, and the implementation formula is:

[0067]

[0068] In the formula, S i (t) represents the spatiotemporal causal feature representation, represents the spatial attenuation factor, d ij Represents the physical distance between nodes i and j.

[0069] In this embodiment, specifically, the quantum annealing environment state prediction module, the quantum annealing energy function construction implementation steps: obtaining the spatiotemporal causal feature representation S i (t) and the edge weights W of the spatiotemporal causal network ij (t), combined with the construction of quantum annealing energy function, the high-dimensional nonlinear relationship of the environmental state is described, and the implementation formula is:

[0070]

[0071] In the formula, E QA represents the quantum annealing energy function, γ represents the longitudinal magnetic field intensity parameter, and controls the contribution weight of the spatiotemporal characteristics to the energy. represents the spin operator, represents the state of node i, η ij represents the coupling coefficient between nodes i and j, reflecting the strength of the causal relationship, represents the spin interaction term between nodes i and j, simulating the effect of causality on the system energy, Δ represents the transverse magnetic field intensity parameter, represents the x-component of the spin operator.

[0072] In this embodiment, specifically, the quantum annealing environment state prediction module has the following steps: according to the quantum annealing energy function, the environment state prediction result at time t+1 is generated through the annealing process evolution, and the implementation formula is:

[0073]

[0074] In the formula, x i (t+1) represents the environmental prediction result, It means finding x that minimizes the objective function i , Δ(t) represents the transverse magnetic field intensity parameter at time t.

[0075] Preferably, the adversarial generative network dynamic correction module generates missing data and performs noise correction based on the original data of the multidimensional ecological signal capture module and the prediction error of the quantum annealing environment state prediction module, and feeds back to the dynamic causal topology construction module;

[0076] Obtain raw environmental data from the multi-dimensional ecological signal capture module, and simultaneously obtain the prediction results and corresponding prediction errors of the quantum annealing environmental state prediction module. Align the raw data and the prediction errors along the time dimension to form a joint input sample, and annotate the missing and noisy areas in the data.

[0077] In this embodiment, a generative adversarial network is constructed, and local segments of the original data and the prediction error are used as conditional information to generate corrected data segments. The conditional generative adversarial network uses the prediction error as a conditional input to guide the generator to learn how to perform corrections in combination with dynamic causal relationships.

[0078] The discriminator compares the original data segment with the corrected data segment output by the generator to determine whether the input data is real data. The multi-layer perceptron convolutional neural network (CNN) extracts the spatiotemporal features of the data and performs binary classification.

[0079] For discriminator training, the generator parameters are fixed, and real data and generated data fragments are input. By minimizing the discriminator's binary classification loss, its ability to distinguish between real and generated data is improved. For generator training, the discriminator parameters are fixed, and the original data and prediction error are input. The generator performance is optimized by maximizing the generator's adversarial loss and reconstruction loss.

[0080] Dynamically adjust the generator's loss function weight according to the magnitude of the prediction error. If the prediction error is large, increase the adversarial loss weight to make the generator pay more attention to global consistency. If the prediction error is small, increase the reconstruction loss weight to ensure the accuracy of local detail correction.

[0081] In this embodiment, specifically, the missing regions of the original data are input into the trained generator, and the prediction error is used as a condition to generate the filled data, and the noise region is smoothed;

[0082] The dynamic causal relationships learned by the generator are used to suppress abnormal fluctuations. Statistical tests are performed on the generated data, and the correction effect is verified using a sliding window. If the error does not converge, the generator parameters are readjusted and the corrected data is combined with the original data to form a new dataset. Missing and noisy regions are given higher confidence weights and are prioritized for causal relationship inference.

[0083] The corrected data is input into the dynamic causal topology construction module to recalculate the causal weights. The corrected data enhances the robustness of causal inference and reduces false causal relationships caused by missing data or noise.

[0084] In this embodiment, specifically, the chaos warning module performs mutation event identification and warning based on the prediction results of the quantum annealing environment state prediction module and the spatiotemporal characteristics of the spatiotemporal correlation deduction module;

[0085] Obtain prediction results from the quantum annealing environment state prediction module and space-time features from the space-time correlation deduction module, align the prediction results with the space-time features in the time and space dimensions, unify the timestamps, and ensure that the predicted values ​​are consistent with the time window of the space-time features;

[0086] Map the predicted values ​​and spatiotemporal features to a unified spatial grid, extract key dynamic indicators from the predicted results, and extract abnormal patterns from the spatiotemporal features;

[0087] Specifically: Using the dynamic relationship between prediction results and spatiotemporal characteristics, we construct a chaotic characteristic index of the mutation event. By comparing the deviation between the predicted value and the actual observed value, we determine whether there is a nonlinear mutation and calculate the consistency between the predicted value and the spatiotemporal characteristics.

[0088] The threshold of mutation events is set according to the distribution of historical data. The static threshold is based on the mean and standard deviation of long-term observation data. The dynamic threshold is combined with the dynamic changes of spatiotemporal characteristics to identify mutation events that exceed the threshold through anomaly detection algorithms. The prediction error, spatiotemporal mismatch, dynamic indicators and other features are input into the classifier to train the mutation event recognition model. The early warning rules are set according to the severity level of the mutation event. The prediction results and the dynamic changes of spatiotemporal characteristics are continuously monitored. The probability of the current mutation event is calculated through a sliding window. If the probability exceeds the preset threshold, an early warning is immediately triggered and an event report is generated.

[0089] In this embodiment, specifically, the dynamic adaptive perception network module dynamically adjusts the sensor layout and sampling frequency based on the prediction results of the quantum annealing environment state prediction module and the warning signal of the chaos warning. The predicted value of the environment state is obtained from the quantum annealing environment state prediction module, and the warning signal of the mutation event is obtained from the chaos warning module. The prediction results and the warning signal are mapped to a unified space-time coordinate system to form dynamic hotspot areas. The regional priorities are divided according to the severity of the warning signal to determine the urgency of sensor adjustment. The sensor layout adjustment problem is converted into a combinatorial optimization problem. The positions of sensor nodes are optimized using the quantum annealing algorithm, and the sensor density is increased in the mutation area indicated by the chaos warning signal.

[0090] In areas where the predicted values ​​show rapid changes in environmental parameters, the sampling frequency is increased, and in areas where the predicted values ​​are stable, the sampling frequency is reduced. When the chaos warning module detects a sudden anomaly, the emergency sampling mode is immediately activated, and the edge computing module is deployed at the sensor node. The sampling rate is dynamically adjusted according to real-time data fluctuations, and the warning signal of the mutation event is fed back to the quantum annealing environmental state prediction module. The parameters of the prediction model are optimized, and the improved prediction results are fed back to the chaos warning module to improve the accuracy of mutation event identification.

[0091] Working Principle: By dividing the grid according to environmental characteristics and deploying multiple heterogeneous sensors, the system uses GPS to unify the time of the entire network, maps the heterogeneous sensor data into a unified spatial coordinate system, and aligns the time dimension through interpolation to eliminate data heterogeneity. The system uses wired and wireless hierarchical transmission.

[0092] By extracting dynamic causal relationships from multidimensional ecological data, a dependency network between environmental variables is constructed. By analyzing time series data, the instantaneous causal effects between nodes are calculated, and the causal strength is dynamically adjusted in combination with an exponential decay factor. Smoothing coefficients and normalization strategies are introduced. The historical causal strength and current observation data are combined to update the edge weights of the causal network to avoid false correlations caused by short-term noise. The regulatory effect of external factors on causal strength is introduced. The spatiotemporal characteristics and causal relationships are jointly modeled to capture the spatial distribution and temporal evolution of environmental variables. The spatial attenuation factor is introduced according to the physical distance to weaken the correlation strength between distant nodes. The causal network edge weights are combined with multidimensional ecological data to generate spatiotemporal causal feature representations to reflect the spatiotemporal consistency of environmental changes. Based on high-dimensional nonlinear relationships, the future environmental state is predicted. The spatiotemporal causal characteristics and the causal network coupling coefficient are converted into a quantum annealing energy function to simulate the complex interactions between environmental variables. Through the characteristics of quantum superposition and entanglement, the global optimal solution of the energy function is quickly searched to generate the prediction results of the future environmental state. The original data is aligned with the prediction error, the missing areas and the noise areas are marked, and the prediction error is predicted through conditional generative adversarial networks. Based on the difference between the predicted and actual values, the algorithm generates corrected data segments and learns dynamic causal relationships to fill the gaps. Deep learning is used to extract spatiotemporal features and distinguish between real and generated data. Based on the magnitude of the prediction error, the global consistency and local accuracy of the generator are dynamically balanced. The corrected data is re-input into the causal topology module, and the causal weights are adjusted. The triggering conditions for mutation events are defined by combining long-term observation data with the dynamic changes in spatiotemporal features. Nonlinear mutations are identified by comparing the deviation between the predicted values ​​and the actual observations. Features such as prediction error and spatiotemporal mismatch are input into the classifier to train a mutation event recognition model. Data is continuously monitored using a sliding window to calculate the mutation probability. If the probability exceeds a threshold, an alert is triggered and an event report is generated. The prediction results and warning signals are mapped to a unified spatiotemporal coordinate system, high-risk areas are demarcated, and the sensor layout problem is transformed into a combinatorial optimization problem. The quantum annealing algorithm is used to solve the optimal node locations. Sensor density is increased in mutation areas. The sampling frequency is increased in areas with rapidly changing environmental parameters and reduced in stable areas to save energy. When a sudden anomaly is detected (such as a fire alarm), the edge computing module's emergency sampling mode is activated, the sampling rate is dynamically increased, and data is fed back to the prediction module in real time.

[0093] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0094] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A real-time dynamic monitoring system for the ecological environment based on machine learning, characterized by: It includes a multi-dimensional ecological signal capture module, a dynamic causal topology construction module, a spatiotemporal correlation deduction module, a quantum annealing environment state prediction module, a dynamic correction module of the adversarial generation network, a dynamic adaptive perception network module, and a chaos early warning module; The dynamic causal topology construction module performs dynamic causal relationship modeling based on the multidimensional environmental data of the multidimensional ecological signal capture module; The spatiotemporal correlation deduction module performs spatiotemporal feature joint modeling based on the causal network of the dynamic causal topology construction module and the multidimensional environmental data of the multidimensional ecological signal capture module; The quantum annealing environment state prediction module performs high-dimensional nonlinear prediction based on the spatiotemporal characteristics of the spatiotemporal correlation deduction module and the causal network of the dynamic causal topology construction module to generate an environment state prediction result.

2. The real-time dynamic monitoring system for ecological environment based on machine learning according to claim 1 is characterized by: The dynamic causal topology building module, the dynamic causal strength evolution step: obtain the time series data X = [x1(t), x2(t), ..., x i (t)],x i (t) represents the observed value of the i-th ecological variable at time t. The dynamic causal strength evolves over time t through the causal influence of node i on node j. The dynamic causal strength evolution is carried out by combining the differential causal effect and the exponential decay factor. The realization formula is: In the formula, C ij (t) represents the dynamic causal strength, represents the instantaneous causal effect of node i on node j, e -λΔt represents the exponential decay factor, η represents the weight of the external regulatory factor, u(t) represents the regulation of the exogenous variable on the dynamic causal intensity, F j Represents the state function of node j, partial derivative Represents x i The unit change of F j impact.

3. The real-time dynamic monitoring system for ecological environment based on machine learning according to claim 2 is characterized by: The dynamic causal topology construction module, dynamic causal key construction and update step: according to the dynamic causal strength, the edge weight update of the dynamic causal network is constructed, and the implementation formula is: In the formula, W ij (t+1) represents the edge weight from node i to j at time t+1, α represents the smoothing coefficient, W ij (t) represents the edge weight of the spatiotemporal causal network, represents the normalized update of the current dynamic causal strength, ∑ k |C ij (t)| represents the normalization factor.

4. The real-time dynamic monitoring system for ecological environment based on machine learning according to claim 1 is characterized by: The spatiotemporal correlation deduction module and the spatiotemporal feature joint modeling implementation steps are: obtaining the multidimensional environmental data x of the multidimensional ecological signal capture module i (t) and the edge weights of the dynamic causal network to generate spatiotemporal causal feature representation, and the implementation formula is: In the formula, S i (t) represents the spatiotemporal causal feature representation, represents the spatial attenuation factor, d ij Represents the physical distance between nodes i and j.

5. The real-time dynamic monitoring system for ecological environment based on machine learning according to claim 1 is characterized by: The quantum annealing environment state prediction module and the quantum annealing energy function construction implementation steps are: obtaining the spatiotemporal causal feature representation S i (t) and the edge weights W of the spatiotemporal causal network ij (t), combined with the construction of quantum annealing energy function, the high-dimensional nonlinear relationship of the environmental state is described, and the implementation formula is: In the formula, E QA represents the quantum annealing energy function, and γ represents the longitudinal magnetic field intensity parameter represents the spin operator, η ij represents the coupling coefficient between nodes i and j, represents the spin interaction term between nodes i and j, Δ represents the transverse magnetic field intensity parameter, represents the x-component of the spin operator.

6. The real-time dynamic monitoring system for ecological environment based on machine learning according to claim 5 is characterized by: The quantum annealing environment state prediction module, the environment state prediction implementation step: according to the quantum annealing energy function, through the annealing process evolution, generates the environment state prediction result at time t+1, and the implementation formula is: In the formula, x i (t+1) represents the environmental prediction result, It means finding x that minimizes the objective function i , Δ(t) represents the transverse magnetic field intensity parameter at time t.

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