A machine learning-based dynamic early warning method and platform for regional ecological carrying capacity

Abstract

This invention discloses a machine learning-based dynamic early warning method and platform for regional ecological carrying capacity. The invention relates to the interdisciplinary field of artificial intelligence and environmental protection, and includes the following steps: 1) using a dual-channel deep decoupling network to separate regional invariant features and regional specific features from the input data; 2) inputting the regional invariant features into a base model to generate a primary early warning signal. This machine learning-based dynamic early warning method and platform for regional ecological carrying capacity eliminates the risk of model failure during cross-regional deployment through a dual-channel feature decoupling mechanism, enabling the early warning system to automatically adapt to different geographical environmental characteristics. The dynamic meta-adaptive architecture achieves rapid response, quickly generating accurate regional compensation strategies in the event of sudden environmental incidents, improving efficiency compared to full model retraining. A dual closed-loop verification system simultaneously ensures compliance with ecological principles and engineering robustness, fundamentally preventing early warning results from violating scientific principles or experiencing delayed responses.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of artificial intelligence and environmental protection, specifically to a method and platform for dynamic early warning of regional ecological carrying capacity based on machine learning. Background Technology

[0002] In the field of dynamic early warning of regional ecological carrying capacity, the application of machine learning models has become a key technological means. Existing technologies typically rely on static early warning models trained on historical data of specific regions. These models are highly dependent on the regional characteristics reflected in the training data, and their inherent learning patterns are deeply coupled with the environmental conditions, resource endowments, and development patterns of specific regions. When attempting to directly transfer such models to new regions with significant differences, the models fail to effectively capture and adapt to the unique ecological elements and constraints of the target region, leading to a significant reduction in the reliability of early warning results and exhibiting serious generalization problems. At the same time, even within the same region, rapid dynamic changes in factors such as the ecological environment, policy regulation, or economic and social activities can cause the data distribution and feature correlations on which the original model relies to drift or become invalid.

[0003] Faced with such changes, existing solutions are often forced to initiate a periodic full retraining process for the entire model. This process not only requires the re-collection and labeling of massive amounts of new data, consuming enormous computing resources and time costs, but more importantly, its response speed lags significantly behind the actual rate of environmental change, resulting in a significant lack of timely warnings and failing to meet the needs of truly dynamic early warning. The cumbersome and inefficient model update mechanism, along with its weak cross-regional adaptability, constitutes the fundamental obstacle faced by existing technologies in achieving efficient and universal dynamic early warning of regional ecological carrying capacity. The current challenge is to enable machine learning early warning models to efficiently adapt to the feature differences between different regions and the rapid dynamic changes of features within the same region, thereby improving the model's generalization ability and real-time early warning accuracy. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution: a dynamic early warning method and platform for regional ecological carrying capacity based on machine learning, comprising the following steps:

[0005] 1) A dual-channel deep decoupling network is used to separate the region-invariant features and region-specific features in the input data;

[0006] 2) Input the regional invariance features into the base model to generate a primary early warning signal;

[0007] 3) Monitor data drift indicators and sudden event signals in the target area through the meta-learning controller, and dynamically generate configuration parameters for the lightweight area adapter;

[0008] 4) Utilize the configured lightweight region adapter to process region-specific features and output a region-adaptive compensation signal;

[0009] 5) The primary early warning signal and the regional adaptive compensation signal are integrated to generate the final early warning result.

[0010] Preferably, the dual-channel deep decoupling network in step 1) achieves orthogonal feature separation in the following way:

[0011] The regional invariance characteristic channel adopts adversarial training constraints to output a cross-regional universal ecological carrying capacity law;

[0012] The region-specific feature channel outputs localized environmental sensitivity factors by minimizing mutual information constraints.

[0013] Preferably, the lightweight area adapter in step 3) is a hypernetwork architecture, and its parameter configuration methods include:

[0014] For incremental data drift, initiate neuron-level incremental fine-tuning;

[0015] For sudden events, the parameters are injected by invoking the matching strategy in the meta-knowledge base to match the scenario.

[0016] Preferably, the meta-knowledge base is constructed in the following manner:

[0017] Store the mapping relationship between historical event feature maps and adaptation strategies;

[0018] A graph neural network is used to associate new validation cases with the updated strategy mapping logic.

[0019] Preferably, a two-level verification mechanism is initiated before step 4):

[0020] Level 1 verification: The primary warning signal output by the base model and the final warning signal output by the complete model are back-mapped to the preset environmental carrying capacity constraint space for compliance comparison;

[0021] Second-level verification: Inject synthetic data streams with preset anomaly patterns into the updated model and monitor changes in early warning response delay and false alarm count.

[0022] Preferably, the compliance comparison of the first-level verification is as follows: when the final warning signal meets all environmental carrying capacity constraints and the primary warning signal violates at least one constraint, the regional adaptation is determined to be valid.

[0023] A machine learning-based dynamic early warning platform for regional ecological carrying capacity includes:

[0024] Data access unit, used for real-time collection of ecological data;

[0025] Feature decoupling processing unit, electrically connected data access unit, built-in dual-channel deep decoupling network;

[0026] The dynamic adaptation engine and the electrical connection feature decoupling processing unit include a meta-learning controller and a lightweight region adapter.

[0027] Verification execution unit, electrically connected to dynamic adaptation engine, used to run two-level verification mechanism;

[0028] The early warning decision unit, the electrical connection feature decoupling processing unit, and the dynamic adaptation engine perform signal fusion output.

[0029] Preferably, the dynamic adaptation engine is further connected to a meta-knowledge autonomous unit, which includes:

[0030] An event-policy mapping database stores the association between adaptation policies and event characteristics;

[0031] Graph neural network processors optimize relationships in real time.

[0032] Preferably, the verification execution unit includes a synthetic data generator and an anomaly monitor:

[0033] The synthetic data generator constructs a data stream carrying a preset exception pattern;

[0034] The anomaly monitor records the time it takes for the model to capture injected anomalies and the number of error warnings.

[0035] Preferably, the platform deployment is decoupled and adapted to a parallel processing architecture:

[0036] The base model processing channel and the region adapter processing channel are independent of each other;

[0037] The early warning decision unit receives dual-channel output and performs fusion calculations.

[0038] This invention provides a method and platform for dynamic early warning of regional ecological carrying capacity based on machine learning. It has the following beneficial effects:

[0039] This machine learning-based dynamic early warning method and platform for regional ecological carrying capacity eliminates the risk of model failure during cross-regional deployment through a dual-channel feature decoupling mechanism, enabling the early warning system to automatically adapt to different geographical environmental characteristics. The dynamic meta-adaptive architecture enables rapid response and quickly generates accurate regional compensation strategies in the event of sudden environmental incidents, improving efficiency compared to retraining the entire model. The dual closed-loop verification system simultaneously ensures compliance with ecological principles and engineering robustness, fundamentally preventing early warning results from violating scientific laws or experiencing delayed responses.

[0040] This machine learning-based dynamic early warning method and platform for regional ecological carrying capacity overcomes the bottleneck of serial processing with its platform-level decoupled-adaptive parallel architecture, enabling ecological pressure analysis and regional compensation calculation to be completed simultaneously, thus improving the timeliness of early warning for major pollution accidents. The hardware fault isolation mechanism ensures that basic early warning functions are maintained under extreme operating conditions, reducing the risk of monitoring gaps caused by system crashes. The knowledge base's autonomous evolution capability forms a cross-regional strategy migration network, enabling remote areas and emerging industrial clusters to obtain early warning accuracy equivalent to that of developed areas, thus promoting ecological governance from passive response to proactive protection. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the module interaction of a machine learning-based dynamic early warning platform for regional ecological carrying capacity according to the present invention.

[0042] Figure 2 This is a flowchart illustrating a dynamic early warning method for regional ecological carrying capacity based on machine learning, according to the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figure 1 and Figure 2 This invention provides a technical solution: a method and platform for dynamic early warning of regional ecological carrying capacity based on machine learning, comprising the following steps:

[0045] 1) A dual-channel deep decoupling network is used to separate the region-invariant features and region-specific features in the input data;

[0046] 2) Input the regional invariance features into the base model to generate a primary early warning signal;

[0047] 3) Monitor data drift indicators and sudden event signals in the target area through the meta-learning controller, and dynamically generate configuration parameters for the lightweight area adapter;

[0048] 4) Utilize the configured lightweight region adapter to process region-specific features and output a region-adaptive compensation signal;

[0049] 5) The primary early warning signal and the regional adaptive compensation signal are integrated to generate the final early warning result.

[0050] It should be further explained that, in the specific implementation process, the real-time collected regional ecological data is first input into a dual-channel deep decoupled network. This network performs feature decoupling operations through parallel processing channels: one channel adopts an adversarial training mechanism to forcibly extract the universal ecological pressure transmission law across regions as regional invariant features; the other channel applies a mutual information minimization constraint to separate regional specific features that are only strongly correlated with local water and soil resources and industrial layout. After the regional invariant features are input into the pre-trained base model to generate a primary early warning signal, a dynamic adaptation mechanism is initiated: the meta-learning controller continuously monitors the changes in the micro-data distribution and macro-level emergencies in the target region. When progressive data drift is detected, only neurons in the lightweight regional adapter that are associated with the drift features are selected for incremental fine-tuning; if a sudden policy adjustment or ecological disaster signal is identified, the adaptation strategy parameters of historical similar events are matched from the meta-knowledge base, calibrated with a small sample, and then injected into the regional adapter.

[0051] Before deploying the adapter, a dual verification process is performed: The first level of verification projects the primary early warning signal output from the base model and the early warning signal to be verified from the complete model back onto an environmental carrying capacity constraint space containing rules such as water and soil resource thresholds and pollution load capacity. Verification is passed only if the signal to be verified fully satisfies the constraints and the primary signal violates the key constraints. The second level of verification injects a synthetic data stream simulating sudden industrial pollution changes into the system. If the updated model captures anomalies within a preset time window and the number of false early warnings does not increase, the update is considered reliable. Finally, at the early warning decision layer, the base model signal and the compensation signal output from the adapter are fused to generate a dynamic early warning result with regional adaptability.

[0052] In step 1), the dual-channel deep decoupling network achieves orthogonal feature separation in the following way:

[0053] The regional invariance characteristic channel adopts adversarial training constraints to output a cross-regional universal ecological carrying capacity law;

[0054] The region-specific feature channel outputs localized environmental sensitivity factors by minimizing mutual information constraints.

[0055] It should be further explained that, in the specific implementation process, during the operation of the dual-channel deep decoupled network, the regional invariant feature channel adopts an adversarial training mechanism to extract universal laws across regions: a discriminator network parallel to the main network is constructed. When ecological data from different geographical and climatic zones are input, the discriminator forcibly identifies the regional identification information in the features. The main channel deceives the discriminator through backpropagation, thereby suppressing the regional correlation in the output features, and ultimately extracting universal laws such as water resource metabolic efficiency and pollution diffusion rate that are not limited by region. At the same time, the regional specific feature channel implements mutual information minimization constraints: the statistical correlation strength between the output features and the invariant features of this channel is calculated in real time, and the mutual information value of the two is made close to the theoretical minimum through gradient descent to ensure the orthogonality of the feature space; for example, when processing data from industrial cities, this channel only retains local unique factors such as the number of pollution discharge permits issued and the capacity utilization rate of characteristic industries, while eliminating general indicators such as GDP energy consumption ratio that overlap with universal laws. The output features of the two channels are verified by the orthogonal verification module: if the cosine similarity between the feature vectors exceeds the threshold, the constraint reinforcement training is triggered until the orthogonality condition is met, thereby ensuring the strictness of decoupling.

[0056] In step 3), the lightweight area adapter is a hypernetwork architecture, and its parameter configuration methods include:

[0057] For incremental data drift, initiate neuron-level incremental fine-tuning;

[0058] For sudden events, the parameters are injected by invoking the matching strategy in the meta-knowledge base to match the scenario.

[0059] It should be further explained that, in the specific implementation process, the lightweight region adapter adopts a hypernetwork architecture to achieve dynamic parameter configuration. Its core lies in the differentiated response mechanism that distinguishes between gradual changes and sudden events. When the meta-learning controller detects that the KL divergence value of the target region feature distribution continuously deviates from the baseline but does not reach the mutation threshold, it is determined to be a gradual data drift. At this time, neuron-level incremental fine-tuning is initiated: first, the subset of adapter neurons that are strongly correlated with the drift features are located, and only the weights of this subset are updated with a small gradient, while the remaining neurons are frozen to maintain the stability of historical knowledge.

[0060] If a sudden surge in policy text keywords or abnormal pollution hotspots are detected in remote sensing imagery, it is identified as a sudden event, triggering the injection of a meta-knowledge base strategy. This involves retrieving the most similar historical cases based on the event feature graph, extracting pre-stored adapter parameters, and then performing small-sample calibration before injection. Real-time data from three days before and after the current sudden event is used to fine-tune the parameter boundary values ​​to ensure the strategy aligns with the real-world scenario. After parameter updates, the mapping relationship between event features and the adapted strategy is automatically recorded to knowledge graph nodes. A graph neural network is then used to analyze the association paths of event types, regional attributes, and effect indicators to optimize future matching priorities.

[0061] The meta-knowledge base is constructed in the following way:

[0062] Store the mapping relationship between historical event feature maps and adaptation strategies;

[0063] A graph neural network is used to associate new validation cases with the updated strategy mapping logic.

[0064] It should be further explained that, in the specific implementation process, the meta-knowledge base autonomously constructs event-policy mapping relationships by fusing and verifying case data in real time. In the initial state, it preloads historical ecological event datasets of typical regions and extracts three-dimensional feature vectors for each type of event: policy text keyword distribution, remote sensing image anomaly patterns, and time-series data mutation points. When a sudden event occurs in a new region, the adaptation strategy generated by the dynamic adaptation engine, after passing two-level verification, automatically triggers the knowledge entry process. First, it parses the event feature fingerprint. If there are records in the feature library with similarity exceeding the threshold, the association weight between that node and the current policy is strengthened; if it is a new type of event, an independent feature node is created and associated with the adaptation policy parameter package. The graph neural network processor periodically scans all policy deployment effectiveness metrics in the knowledge base and initiates failure detection for policy nodes with rising false alarm rates or increased response delays. It traces the historical event feature change trends associated with the policy and automatically decouples outdated mapping relationships when core features deviate from the original pattern. Simultaneously, based on the common features of newly added validation cases, it autonomously generates cross-regional policy migration rules. For example, when the "sudden drop in groundwater level" event in both arid and semi-arid regions is applicable to the same type of water-saving industry regulation parameters, it establishes a second-order correlation edge between regional type and policy effectiveness.

[0065] Before executing step 4), a two-level verification mechanism is initiated:

[0066] Level 1 verification: The primary warning signal output by the base model and the final warning signal output by the complete model are back-mapped to the preset environmental carrying capacity constraint space for compliance comparison;

[0067] Second-level verification: Inject synthetic data streams with preset anomaly patterns into the updated model and monitor changes in early warning response delay and false alarm count.

[0068] It should be further explained that, in the specific implementation process, a two-level verification process is initiated before deploying the regional adaptation strategy. The first level of verification performs an ecological principle compliance check: the primary warning signal output by the base model and the warning signal to be verified generated by the complete model are simultaneously input into the environmental carrying capacity constraint space for projection mapping. This constraint space pre-sets rigid rules such as a set of inequalities for sustainable use of water and soil resources and a threshold chain for biodiversity protection. The regional adaptation is deemed effective only when the signal to be verified meets all constraints and the primary signal violates key constraints. For example, if the warning value for water resource consumption in a newly built petrochemical park exceeds the "30% annual runoff" red line in the base model, and the warning value falls back to within the red line after adaptation, the verification is passed. The second level of verification conducts an engineering robustness stress test on the updated model: the synthetic data generator constructs a data stream carrying preset anomaly patterns, including pollutant concentration peak sequences simulating sudden pollution accidents and adversarial samples of ecological element correlation breaks. After injection, the model's response performance is monitored within a preset time window. If all preset anomalies are captured and the number of false alarms in routine monitoring does not increase, the update is confirmed to be reliable. The two-level verification has a strict progressive relationship: the second level can only be started after the first level verification is passed. If either step fails, the adaptation strategy will be rolled back and a diagnostic report will be generated.

[0069] The compliance comparison for the first-level verification is as follows: when the final warning signal satisfies all environmental carrying capacity constraints and the primary warning signal violates at least one constraint, the regional adaptation is deemed valid. It should be further explained that, in the specific implementation process, during the ecological constraint spatial projection verification, the primary warning signal generated by the base model and the regionally adapted signal to be verified are synchronously mapped to a pre-set multi-dimensional rule matrix. This matrix integrates three core constraint systems: the water and soil resource sustainability inequality, the biodiversity conservation threshold chain, and the pollution load capacity boundary.

[0070] When performing compliance comparison, the first step is to verify whether the signal to be verified meets all the constraints. If any violation occurs, it is directly deemed invalid. For primary warning signals, validity is only triggered when they break through key rigid constraints. For example, if the base model output shows that the groundwater extraction of a certain watershed exceeds 40% of the average annual replenishment, and the result after adaptation falls back to within the 30% safety threshold, then the regional adaptation is deemed valid.

[0071] In special scenarios, a weighted approach to constraint priorities is implemented: the weight of biodiversity constraints is strengthened in ecologically fragile areas, and the focus is on pollution load boundary verification in industrial clusters; when a primary signal violates the constraints but the signal to be verified is in a critical state, constraint source tracing analysis is initiated to locate the core ecological factors causing the difference and verify their regional relevance, and approval is granted after confirmation.

[0072] A machine learning-based dynamic early warning platform for regional ecological carrying capacity includes:

[0073] Data access unit, used for real-time collection of ecological data;

[0074] Feature decoupling processing unit, electrically connected data access unit, built-in dual-channel deep decoupling network;

[0075] The dynamic adaptation engine and the electrical connection feature decoupling processing unit include a meta-learning controller and a lightweight region adapter.

[0076] Verification execution unit, electrically connected to dynamic adaptation engine, used to run two-level verification mechanism;

[0077] The early warning decision unit, the electrical connection feature decoupling processing unit, and the dynamic adaptation engine perform signal fusion output.

[0078] It should be further explained that, in the specific implementation process, the platform collects ecological data in real time through the multi-source heterogeneous interface of the data access unit, including signals from physical devices such as temperature and humidity sensors at meteorological stations, COD probes in water quality monitors, and satellite remote sensing spectrometers. After preprocessing by edge computing nodes, the data is transmitted to the feature decoupling processing unit. This unit is equipped with a dedicated neural network processor and runs a dual-channel deep decoupling network: channel one uses an adversarial training chipset to extract universal features across regions, while channel two is configured with a mutual information constraint acceleration card to separate region-specific features.

[0079] The dynamic adaptation engine connects to the decoupling unit via a high-speed data bus. Its meta-learning controller has a built-in KL divergence calculation module and a text-image mutation detector. When progressive drift is detected, the FPGA programmable logic unit is triggered to incrementally fine-tune a specified subset of neurons in the lightweight region adapter. If a sudden event is detected, the knowledge base interface is called to inject a pre-stored strategy.

[0080] A two-way feedback link is established between the verification execution unit and the adaptation engine, and the ecological constraint projection processor and the synthetic data injection port are started for collaborative verification before the policy is deployed.

[0081] The early warning decision unit receives the base model signal from the decoupling unit and the compensation signal from the adaptation engine, performs weighted saturation calculation in the fusion processor, and finally outputs a red / orange / yellow three-color dynamic early warning signal through the multi-level early warning interface.

[0082] The dynamic adaptation engine is further connected to the meta-knowledge autonomous unit, which includes:

[0083] An event-policy mapping database stores the association between adaptation policies and event characteristics;

[0084] Graph neural network processors optimize relationships in real time.

[0085] It should be further explained that, in the specific implementation process, the meta-knowledge autonomous unit parses the input data stream in real time through an event feature extractor to construct a three-dimensional event feature fingerprint: capturing keyword frequency mutation rate from the policy text stream, extracting morphological parameters of abnormal areas from the remote sensing data stream, and calculating gradient mutation points of ecological indicators from the sensor network. The event-policy mapping database adopts a distributed graph storage architecture, with each policy node associated with a four-dimensional effect evaluation vector: early warning response delay, false alarm suppression rate, regional adaptability, and knowledge reuse frequency.

[0086] The graph neural network processor periodically performs knowledge topology optimization: when the false alarm suppression rate of a policy node continuously decreases or its fitness falls below a critical value, it initiates policy failure tracing, analyzes the drift trajectory of associated event feature fingerprints, and automatically decouples outdated mapping relationships; simultaneously, based on the cross-regional commonalities of new cases, it generates policy migration rules and strengthens the connection weights of nodes in the target region. At the hardware level, a feature fingerprint coprocessor and a graph computing accelerator card are deployed to ensure the speed of knowledge iteration. When knowledge conflicts occur, security isolation is initiated: contradictory policies are transferred to a sandbox environment for verification, retaining only the version with the higher overall performance evaluation score.

[0087] The verification execution unit includes a synthetic data generator and an anomaly monitor.

[0088] The synthetic data generator constructs a data stream carrying a preset exception pattern;

[0089] The anomaly monitor records the time it takes for the model to capture injected anomalies and the number of error warnings.

[0090] It should be further explained that, in the specific implementation process, the synthetic data generator in the verification execution unit has a built-in multimodal anomaly pattern library. When the dynamic adaptation engine submits an update request, it automatically selects an anomaly template that matches the ecological characteristics of the target area to construct the test data stream. For industrially dominated areas, time-series data simulating toxic substance leaks is generated: benzene series concentration pulse peaks are superimposed on a normal water quality monitoring substrate. The peak morphology strictly reproduces the diffusion curve characteristics of historical accidents, while maintaining the chemical equilibrium constraints of related parameters such as pH value.

[0091] In ecological protection zones, data on food chain disruptions are injected: maintaining normal growth in vegetation cover while simultaneously configuring an abnormal decline in bird migration numbers, creating an ecological logical contradiction. After data injection, the anomaly monitor initiates multi-dimensional tracking: recording the capture delay of preset anomaly patterns (i.e., the time difference between injection and red alert triggering), statistically analyzing the false alarm increment during regular monitoring cycles (i.e., comparing the change in the number of false alerts within three days before and after the update), and real-time analysis of whether the model's decision path points to preset pollution source location areas or key factors of species extinction. When a capture delay exceeds the timeout or the false alarm increment exceeds the historical fluctuation range, a fault tracing report is automatically generated: marking the unresponsive anomaly pattern characteristic points, the time period of false alarm growth, and the deviation of the decision path, triggering a strategy rollback mechanism, and sending a pulse alarm to the operation and maintenance terminal via an optical signal alarm.

[0092] Platform deployment decoupling - adapting to parallel processing architecture:

[0093] The base model processing channel and the region adapter processing channel are independent of each other;

[0094] The early warning decision unit receives dual-channel output and performs fusion calculations.

[0095] It should be further explained that, in the specific implementation process, the platform adopts a physically isolated dual-channel processing architecture to achieve decoupling and parallelization of adaptation. The base model processing channel is equipped with a dedicated computing card to continuously run the regional invariant feature analysis and output a unified ecological pressure index for the entire region. The regional adapter processing channel dynamically processes specific features through programmable logic units to generate compensation factors for the target region.

[0096] The dual channels synchronously transmit results to the early warning decision unit via independent data buses at the hardware layer. When a sudden pollution event occurs in an industrial area, the base model channel outputs the "risk index of cross-regional diffusion of toxic substances," while the adapter channel simultaneously generates the "correction coefficient for the self-purification capacity of local rivers." The decision unit performs a saturated fusion calculation: a preset threshold constraint is applied to the compensation factor to prevent over-correction due to abnormal river characteristic data. When an ecological reserve encounters drought, the base model channel provides the output of the "general vegetation water stress model," while the adapter channel calculates the "drought resistance coefficient of endemic species." Dynamic weight allocation is initiated during fusion, and the drought resistance coefficient weight is automatically increased to 0.8 when the activity trajectory of endangered species is detected. In fault scenarios, channel self-checks are initiated: if the sudden value output by the adapter channel exceeds three standard deviations of historical fluctuations, it automatically switches to the base model independent operation mode and triggers an optically encoded alarm to indicate a local failure.

[0097] It should be further explained that, in the specific implementation process, regional ecological data is collected in real time through a multi-source sensor network, including physical signals such as temperature and humidity readings from meteorological stations, dissolved oxygen concentration from water quality monitors, and vegetation indices from satellite remote sensing. After data access, the data first enters the feature decoupling processing stage, which uses a dual-channel deep neural network architecture to separate regional commonalities from specific characteristics. The commonality channel uses an adversarial training mechanism to remove regionally sensitive information. Specifically, a parallel discriminator is set up to identify geographic identifiers, and the weights of these features are continuously weakened during backpropagation in the main network, ultimately outputting universal cross-regional laws such as water resource metabolic efficiency and pollution diffusion rate. The characteristic channel is subject to mutual information minimization constraints, and the statistical correlation between the output features of this channel and the commonalities is calculated in real time. Gradient descent is used to forcibly reduce the correlation strength, ensuring that unique regional factors such as local water and soil resource parameters and pollution coefficients of characteristic industries are extracted purely. The output results of the two channels need to be tested by an orthogonality verification module. If the similarity of the feature vectors exceeds a set threshold, reinforcement training is triggered until the target is met.

[0098] While common features are input into the pre-trained base model to generate initial early warning signals, the meta-learning controller continuously scans the data stream of the target area. When slow changes in indicators such as industrial water reuse rate are detected, it is determined to be a gradual drift, and a neuron-level fine-tuning operation is initiated: precisely locate the subset of functional neurons in the region adapter associated with the drift indicator, perform only minor parameter updates on this subset, and freeze the rest. If a sudden policy adjustment or ecological disaster signal is identified, the meta-knowledge base is invoked to match historical event strategies: based on the similarity of event feature fingerprints, an appropriate parameter package is retrieved, and the parameter boundary values ​​are calibrated using short-term data before and after the current event before injection.

[0099] Before any adaptation strategy can be deployed, it must undergo a two-stage verification process: The first stage maps the primary signal from the base model to the signal to be verified in the complete model onto a space of rigid ecological rules, such as the sustainability inequality for water and soil resources and the chain threshold for biodiversity, for compliance comparison. Verification is only successful if the signal to be verified fully meets the constraints and the primary signal violates the key rules. The second stage injects synthetic data streams simulating pollution incidents into the system, monitoring the model's response time to preset anomaly patterns and fluctuations in the number of common false alarms. Only after both criteria are met can the new strategy be activated.

[0100] The early warning decision-making layer receives the ecological pressure index output by the base model and the regional compensation factor generated by the adapter, and then performs a fusion calculation. In the case of sudden pollution in industrial areas, an upper limit constraint is imposed on the river self-purification correction coefficient to prevent over-correction; in the case of drought events in ecological protection areas, the drought resistance coefficient weight is automatically increased when the activity trajectory of endangered species is detected.

[0101] The platform employs a physically isolated dual-channel architecture to ensure real-time performance: the base model processing channel is equipped with a dedicated computing card for continuous operation, while the adapter channel is dynamically reconfigured via a programmable logic unit. The dual-channel outputs are synchronously transmitted to the fusion processor via independent buses. If the output value of either channel exceeds the historical fluctuation range, fault isolation is triggered: the abnormal channel is frozen, the system switches to the base model independent operation mode, and an optically encoded alarm is sent.

[0102] The meta-knowledge base autonomously constructs policy mapping relationships through an event feature extractor. The frequency mutation rate of industrial policy keywords in policy texts, the change in the curvature of the contour of polluted areas in remote sensing images, and the gradient mutation points of sensor network indicators constitute a three-dimensional feature fingerprint.

[0103] Each validated strategy update triggers knowledge base optimization: For strategy nodes experiencing performance degradation, the drift trajectory of associated event features is traced, automatically decoupling failure mappings; strategy migration rules are generated based on the commonalities of cross-regional cases. A graph neural network processor periodically scans the knowledge topology, with hardware-level accelerator cards ensuring iteration speed. In the event of knowledge conflicts, a sandbox environment is initiated for validation, injecting historical data streams to test the overall strategy performance score, and retaining the most effective versions.

[0104] The synthetic data generator incorporates scenario-based test templates. Industrial zone testing employs pulse superposition technology for toxic substance concentrations to rigorously reproduce the diffusion curve morphology of leak accidents; ecological protection zones construct a logical contradiction: normal growth in vegetation index but abnormal decline in species numbers. During stress testing, the anomaly monitor tracks three core indicators: the preset anomaly capture delay must be a specific multiple shorter than the standard response cycle; fluctuations in the number of routine false alarms must not exceed the historical normal range; and key nodes in the decision-making path must fully cover the core factors of pollution source location or species extinction.

[0105] If any indicator exceeds the limit, a three-level traceability report will be generated: the primary level locates the timestamp of abnormal data, the intermediate level identifies the missing decision nodes, and the advanced level identifies the omissions in factor extraction from the feature decoupling module, providing accurate basis for rapid repair.

[0106] It should be further explained that, in the specific implementation process, a dynamic early warning method for regional ecological carrying capacity based on machine learning includes the following steps:

[0107] Step S1: Collect real-time data streams of temperature, humidity, pollutant concentration, and vegetation coverage index of the target area through meteorological sensors, water quality monitors, and satellite remote sensing equipment. After preprocessing by edge nodes, the data streams are input into the feature decoupling network.

[0108] Step S2: Use a dual-channel deep network to separate features: Channel 1 performs adversarial training to remove geographic identification information from the data and output the universal ecological pressure transmission law across regions; Channel 2 is subject to mutual information minimization constraint to suppress the statistical correlation between its output and Channel 1, and purely extracts regionally unique factors such as local water and soil resource parameters and pollution coefficients of characteristic industries; the orthogonal verification module verifies the correlation between the output feature vectors of the two channels, and triggers reinforcement training when the threshold is exceeded.

[0109] Step S3: Input the universal features output from Channel 1 into the pre-trained base model to generate primary early warning signals such as a unified water resource scarcity index and pollution diffusion risk value across the entire domain;

[0110] Step S4: Event-driven dynamic parameter configuration: When the meta-learning controller detects slow changes in indicators such as industrial water reuse rate, the subset of associated neurons in the location area adapter performs micro-incremental updates; if a sudden change in policy keywords or remote sensing anomalies are identified, historical event strategy parameter packages are matched from the meta-knowledge base, calibrated with current short-term data, and injected into the adapter; the adapter outputs regional compensation signals such as the self-purification correction coefficient of industrial zone river channels and drought resistance factor of protected species.

[0111] Step S5: Progressive verification strategy, including: Level 1 ecological compliance test: Map the primary signal of the base model and the signal to be verified to the rule space such as the water and soil resource inequality set and the biodiversity chain threshold. It passes only when the signal to be verified is fully compliant and the primary signal violates the key constraints; Level 2 engineering robustness test: Inject synthetic data stream simulating chemical leakage into the system, monitor the preset anomaly capture delay and the fluctuation of the number of regular false alarms, and activate the new strategy only if both standards are met.

[0112] Step S6: The ecological pressure index output by the base model and the regional compensation factor are input into the fusion processor in parallel; in the industrial zone scenario, an upper limit constraint is imposed on the pollution correction coefficient to prevent data anomalies from causing over-correction; when the trajectory of endangered species is detected in the ecological reserve, the drought resistance factor is automatically increased to the dominant position.

[0113] Step S7: Extract feature fingerprints such as the frequency mutation rate of policy keywords and the change in the curvature of the contaminated area contour of new events; when the false alarm suppression rate of the strategy node continues to decrease, trace the drift trajectory of related event features and decouple the failure mapping; generate strategy migration rules based on the commonalities of cross-regional cases to strengthen the connection weight of nodes in the target region;

[0114] Step S8: The base model channel is run by a dedicated computing card, and the adapter channel is dynamically reconfigured through a programmable unit; the fluctuation range of the channel output value is monitored in real time, and the abnormal channel is frozen when it exceeds the historical threshold, and the system switches to the base model independent operation mode; a pulse light coding alarm is triggered and a fault location report is generated.

[0115] By employing a dual-channel feature decoupling mechanism, the risk of model failure during cross-regional deployment is eliminated, enabling the early warning system to automatically adapt to different geographical environmental characteristics. The dynamic meta-adaptive architecture enables rapid response, quickly generating accurate regional compensation strategies in the event of sudden environmental incidents, which improves efficiency compared to retraining the entire model. The dual closed-loop verification system simultaneously ensures compliance with ecological principles and engineering robustness, fundamentally preventing early warning results from violating scientific laws or experiencing delayed responses.

[0116] Platform-level decoupling and parallel architecture breaks through the bottleneck of serial processing, enabling ecological pressure analysis and regional compensation calculation to be completed simultaneously, improving the timeliness of early warning of major pollution accidents; hardware fault isolation mechanism ensures that basic early warning functions are maintained under extreme operating conditions, reducing the risk of monitoring gaps caused by system crashes; the knowledge base's autonomous evolution capability forms a cross-regional strategy migration network, enabling remote areas and emerging industrial clusters to obtain the same early warning accuracy as developed areas, promoting ecological governance from passive response to proactive protection.

[0117] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0118] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic early warning method for regional ecological carrying capacity based on machine learning, characterized in that, Includes the following steps: 1) A dual-channel deep decoupling network is used to separate the region-invariant features and region-specific features in the input data; 2) Input the regional invariance features into the base model to generate a primary early warning signal; 3) Monitor data drift indicators and sudden event signals in the target area through the meta-learning controller, and dynamically generate configuration parameters for the lightweight area adapter; 4) Utilize the configured lightweight region adapter to process region-specific features and output a region-adaptive compensation signal; 5) The initial warning signal and the regional adaptive compensation signal are integrated to generate the final warning result; In step 1), the dual-channel deep decoupling network achieves orthogonal feature separation in the following way: The regional invariance characteristic channel adopts adversarial training constraints to output a cross-regional universal ecological carrying capacity law; The region-specific feature channel outputs localized environmental sensitivity factors through mutual information minimization constraints. In step 3), the lightweight area adapter is a hypernetwork architecture, and its parameter configuration methods include: For incremental data drift, initiate neuron-level incremental fine-tuning; For sudden events, the parameters are injected by invoking the matching strategy in the meta-knowledge base to match the scenario.

2. The method for dynamic early warning of regional ecological carrying capacity based on machine learning according to claim 1, characterized in that: The meta-knowledge base is constructed in the following way: Store the mapping relationship between historical event feature maps and adaptation strategies; A graph neural network is used to associate new validation cases with the updated strategy mapping logic.

3. The method for dynamic early warning of regional ecological carrying capacity based on machine learning according to claim 1, characterized in that: Before executing step 4), a two-level verification mechanism is initiated: Level 1 verification: The primary warning signal output by the base model and the final warning signal output by the complete model are back-mapped to the preset environmental carrying capacity constraint space for compliance comparison; Second-level verification: Inject synthetic data streams with preset anomaly patterns into the updated model and monitor changes in early warning response delay and false alarm count.

4. The method for dynamic early warning of regional ecological carrying capacity based on machine learning according to claim 3, characterized in that: The compliance comparison for the first level of verification is as follows: when the final warning signal meets all environmental carrying capacity constraints and the primary warning signal violates at least one constraint, the regional adaptation is deemed valid.

5. A machine learning-based dynamic early warning platform for regional ecological carrying capacity, used to implement the method as described in any one of claims 1 to 4, characterized in that, include: Data access unit, used for real-time collection of ecological data; Feature decoupling processing unit, electrically connected data access unit, built-in dual-channel deep decoupling network; Dynamic adaptation engine, electrical connection feature decoupling processing unit, including meta-learning controller and lightweight region adapter; Verification execution unit, electrically connected to dynamic adaptation engine, used to run two-level verification mechanism; The early warning decision unit, the electrical connection feature decoupling processing unit, and the dynamic adaptation engine perform signal fusion output.

6. The regional ecological carrying capacity dynamic early warning platform based on machine learning according to claim 5, characterized in that: The dynamic adaptation engine is further connected to the meta-knowledge autonomous unit, which includes: An event-policy mapping database stores the association between adaptation policies and event characteristics; Graph neural network processors optimize relationships in real time.

7. The regional ecological carrying capacity dynamic early warning platform based on machine learning according to claim 6, characterized in that: The verification execution unit includes a synthetic data generator and an anomaly monitor. The synthetic data generator constructs a data stream carrying a preset exception pattern; The anomaly monitor records the time it takes for the model to capture injected anomalies and the number of error warnings.

8. The regional ecological carrying capacity dynamic early warning platform based on machine learning according to claim 7, characterized in that: Platform deployment decoupling - adapting to parallel processing architecture: The base model processing channel and the region adapter processing channel are independent of each other; The early warning decision unit receives dual-channel output and performs fusion calculations.

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