Regional ecological bearing capacity dynamic early warning method and platform based on machine learning

Abstract

The invention discloses a regional ecological bearing capacity dynamic early warning method and platform based on machine learning, and relates to the technical field of artificial intelligence and environmental protection crossing, and the method comprises the following steps: 1) employing a dual-channel deep decoupling network to separate regional invariance features and regional specificity features in input data; 2) inputting the regional invariance characteristics into the base model to generate a primary early warning signal; according to the regional ecological bearing capacity dynamic early warning method and platform based on machine learning, a model failure risk during cross-regional deployment is eliminated through a dual-channel feature decoupling mechanism, so that an early warning system automatically adapts to different geographical environment features; the dynamic meta-adaptation architecture realizes quick response, a precise area compensation strategy is quickly generated in an emergent environment event, and compared with full-model retraining, the efficiency is improved; the double-closed-loop verification system synchronously guarantees the compliance of the ecological principle and the engineering robustness, and the problem that the early warning result violates the scientific rule or the response is lagged is completely eradicated from the source.

Description

Technical Field

[0001] The present invention relates to the technical field of intersection between artificial intelligence and environmental protection, and specifically to a dynamic early warning method and platform for regional ecological carrying capacity based on machine learning. Background Art

[0002] In the field of dynamic early warning of regional ecological carrying capacity, the application of machine learning models has become a key technical means. Existing technologies usually rely on static early warning models trained based on historical data of a specific region. Such models are highly dependent on the regional characteristics reflected in the training data, and their inherent law learning is deeply coupled with the environmental conditions, resource endowments and development models of a specific region. When attempts are made to directly transfer such models to new regions with significant differences, the models fail to effectively capture and adapt to the unique ecological factors and constraints of the target region, resulting in a significant reduction in the reliability of the early warning results and a serious lack of generalization ability. 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 associations that the original model relies on to drift or fail.

[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 annotation of massive amounts of new data, consuming enormous computing resources and time costs, but more importantly, its response speed lags seriously behind the actual rate of environmental change, resulting in a significant lack of warning timeliness and an inability to meet the needs of truly dynamic warnings. The cumbersome and inefficient model update mechanism and its weak cross-regional adaptability together constitute the fundamental obstacles faced by existing technologies in achieving efficient and universal dynamic warnings of regional ecological carrying capacity. The current problem to be solved is: how to enable machine learning warning models to efficiently adapt to the characteristic differences between different regions and the rapid dynamic changes of characteristics within the same region, so as to improve the model's generalization ability and real-time warning accuracy. Summary of the Invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method and platform for dynamic early warning of regional ecological carrying capacity based on machine learning, comprising the following steps: 1) A dual-channel deep decoupling network is used to separate region-invariant features from region-specific features in the input data; 2) Inputting regional invariance features into the base model to generate primary warning signals; 3) Using a meta-learning controller to monitor data drift indicators and emergency event signals in the target area, dynamically generating configuration parameters for the lightweight regional adapter; 4) Using the configured lightweight regional adapter to process region-specific features and output a region-adaptive compensation signal; 5) The primary warning signal is integrated with the regional adaptive compensation signal to generate the final warning result.

[0005] Preferably, the dual-channel deep decoupling network in step 1) achieves feature orthogonal separation by: The regional invariance feature channel uses adversarial training constraints to output universal ecological carrying capacity laws across regions; The region-specific feature channel outputs localized environmental sensitive factors through mutual information minimization constraints.

[0006] Preferably, the lightweight regional adapter in step 3) is a super network architecture, and its parameter configuration method includes: Enable neuron-level incremental fine-tuning for gradual data drift; For sudden events, the adaptation strategy injection parameters of the matching scenario in the meta-knowledge base are called.

[0007] Preferably, the meta-knowledge base is constructed in the following manner: Store the mapping relationship between historical event feature maps and adaptation strategies; Graph neural networks are used to associate new verification cases to update the strategy mapping logic.

[0008] Preferably, a two-level verification mechanism is activated before step 4) is executed: First-level verification: The primary warning signal output by the base model and the final warning signal output by the complete model are reversely mapped to the preset environmental carrying capacity constraint space for compliance comparison; Second-level verification: Inject synthetic data streams with preset abnormal patterns into the updated model to monitor changes in early warning response delays and the number of false alarms.

[0009] Preferably, the compliance comparison of the first-level verification is: when the final warning signal satisfies all environmental carrying capacity constraints and the primary warning signal violates at least one constraint, it is determined that the regional adaptation is valid.

[0010] A dynamic early warning platform for regional ecological carrying capacity based on machine learning, including: Data access unit, used to collect ecological data in real time; Feature decoupling processing unit, electrically connected to the data access unit, with a built-in dual-channel deep decoupling network; Dynamic adaptation engine, electrical connection feature decoupling processing unit, including meta-learning controller and lightweight regional adapter; A verification execution unit, electrically connected to the dynamic adaptation engine, for running a two-stage verification mechanism; The early warning decision unit electrically connects the feature decoupling processing unit and the dynamic adaptation engine to perform signal fusion output.

[0011] Preferably, the dynamic adaptation engine is further connected to a meta-knowledge autonomous unit, which includes: Event-strategy mapping database, which stores the association between adaptation strategies and event characteristics; Graph neural network processor, optimizing association relationships in real time.

[0012] Preferably, the verification execution unit includes a synthetic data generator and an anomaly monitor: The synthetic data generator constructs a data stream carrying a preset abnormal pattern; The anomaly monitor records the time it takes for the model to capture injected anomalies and the number of false warnings.

[0013] Preferably, the platform deploys a decoupling-adaptive parallel processing architecture: The base model processing channel and the regional adapter processing channel are independent of each other; The early warning decision unit receives the dual-channel output and performs fusion operations.

[0014] 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: 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 environment characteristics; the dynamic meta-adaptation architecture achieves rapid response and quickly generates accurate regional compensation strategies in sudden environmental events, improving efficiency compared to full model retraining; the dual closed-loop verification system simultaneously ensures compliance with ecological principles and engineering robustness, eliminating the problem of early warning results violating scientific laws or delayed responses from the root.

[0015] This machine learning-based dynamic early warning method and platform for regional ecological carrying capacity uses a platform-level decoupling-adaptation parallel architecture to break through the serial processing bottleneck, enabling simultaneous completion of ecological pressure analysis and regional compensation calculations, thereby improving the timeliness of early warnings for major pollution accidents. The hardware fault isolation mechanism ensures the maintenance of basic early warning functions under extreme working conditions, reducing the risk of monitoring gaps caused by system crashes. The autonomous evolution capability of the knowledge base forms a cross-regional strategy migration network, enabling remote areas and emerging industrial clusters to obtain the same early warning accuracy as developed regions, thereby promoting ecological governance from passive response to active protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of module interaction of a regional ecological carrying capacity dynamic early warning platform based on machine learning in the present invention; Figure 2 This is a flow chart of a dynamic early warning method for regional ecological carrying capacity based on machine learning according to the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] See also Figure 1 and Figure 2 The present 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: 1) A dual-channel deep decoupling network is used to separate region-invariant features from region-specific features in the input data; 2) Inputting regional invariance features into the base model to generate primary warning signals; 3) Using a meta-learning controller to monitor data drift indicators and emergency event signals in the target area, dynamically generating configuration parameters for the lightweight regional adapter; 4) Using the configured lightweight regional adapter to process region-specific features and output a region-adaptive compensation signal; 5) The primary warning signal is integrated with the regional adaptive compensation signal to generate the final warning result.

[0019] It is important to further clarify that, during implementation, real-time regional ecological data is first fed into a dual-channel deep decoupling network, which performs feature decoupling via parallel processing channels. One channel employs an adversarial training mechanism to extract universal cross-regional ecological pressure transmission patterns as regionally invariant features; the other channel imposes a mutual information minimization constraint to isolate region-specific features that are strongly correlated only with local water and soil resources and industrial layout. After inputting these regionally invariant features into a pre-trained base model to generate a primary warning signal, a dynamic adaptation mechanism is initiated. A meta-learning controller continuously monitors changes in the target region's micro-data distribution and macro-level emergencies. When progressive data drift is detected, only neurons associated with the drifting features in the lightweight regional adapter are incrementally fine-tuned. If a sudden policy adjustment or ecological disaster signal is identified, adaptation strategy parameters from similar historical events are matched from the meta-knowledge base and injected into the regional adapter after small-sample calibration.

[0020] Before deploying the adapter, a double validation process is performed. The first level of validation projects the primary warning signal from the base model and the unverified warning signal 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 unverified signal fully satisfies the constraints and the primary signal violates key constraints. The second level of validation injects a synthetic data stream simulating sudden changes in industrial pollution into the system. If the updated model captures anomalies within a preset time window and the number of false warnings does not increase, the update is deemed reliable. Finally, at the warning decision layer, the base model signal and the compensation signal from the adapter are fused to generate dynamic warning results with regional adaptability.

[0021] In step 1), the dual-channel deep decoupling network achieves feature orthogonal separation in the following ways: The regional invariance feature channel uses adversarial training constraints to output universal ecological carrying capacity laws across regions; The region-specific feature channel outputs localized environmental sensitive factors through mutual information minimization constraints.

[0022] It is important to further clarify that, during the implementation, the regional invariant feature channel employs an adversarial training mechanism to extract cross-regional universal patterns within the dual-channel deep decoupling network. This involves constructing a discriminator network parallel to the main network. When fed ecological data from different geographical and climatic regions, the discriminator is forced to recognize the regional identifiers in the features. The main channel uses backpropagation to deceive the discriminator, thereby suppressing regional correlations in the output features. Ultimately, this approach extracts universal patterns that are not restricted by geographical location, such as water resource metabolic efficiency and pollution diffusion rate. Simultaneously, the regionally specific feature channel implements a mutual information minimization constraint: The statistical correlation strength between the channel's output features and the invariant features is calculated in real time. Gradient descent is used to minimize the mutual information between the two, ensuring orthogonality in the feature space. For example, when processing data from industrial cities, this channel retains only local factors such as the number of pollution discharge permits issued and the capacity utilization rate of specialized industries, while eliminating common indicators such as the GDP-to-energy ratio that overlap with universal patterns. The output features of the two channels are verified by the orthogonality verification module: if the cosine similarity between the feature vectors exceeds the threshold, constrained reinforcement training is triggered until the orthogonality condition is met, thereby ensuring the strictness of the decoupling.

[0023] The lightweight regional adapter in step 3) is a super network architecture, and its parameter configuration method includes: Enable neuron-level incremental fine-tuning for gradual data drift; For sudden events, the adaptation strategy injection parameters of the matching scenario in the meta-knowledge base are called.

[0024] It's important to note that, in its implementation, the lightweight regional adapter utilizes a hypernetwork architecture for dynamic parameter configuration. Its core principle is to differentiate between gradual changes and sudden events through a differentiated response mechanism. When the meta-learning controller detects that the KL divergence of the target region's feature distribution consistently deviates from the baseline but does not reach the mutation threshold, it identifies this as gradual data drift. At this point, neuron-level incremental fine-tuning is initiated: First, a subset of adapter neurons strongly associated with the drifting features is identified, and only a small gradient update is performed on the weights of this subset. The remaining neurons are frozen to maintain historical knowledge stability.

[0025] If a sudden increase in the frequency of keywords in policy texts or an unusual pollution hotspot is detected in remote sensing imagery, it is identified as an emergency, triggering the injection of a meta-knowledge base strategy. Based on the event feature graph, the most similar historical cases are retrieved. After extracting pre-stored adapter parameters, they are calibrated with a small sample before injection. The parameter boundaries are fine-tuned using real-time data from three days before and after the current emergency to ensure that the strategy aligns with the real-world scenario. After the parameter update is complete, the mapping relationship between event features and adaptation strategies is automatically recorded and added to the knowledge graph node. A graph neural network is then used to analyze the associated paths of event types, regional attributes, and effect indicators to optimize future matching priorities.

[0026] The meta-knowledge base is constructed in the following way: Store the mapping relationship between historical event feature maps and adaptation strategies; Graph neural networks are used to associate new verification cases to update the strategy mapping logic.

[0027] It's important to further clarify that, during its implementation, the meta-knowledge base autonomously constructs event-strategy mappings through real-time integration of verified case data. Initially, it preloads historical ecological event datasets from representative regions and extracts three-dimensional feature vectors for each type of event: policy text keyword distribution, remote sensing imagery anomaly patterns, and time series data mutation points. When an emergency occurs in a new region, the adaptation strategy generated by the dynamic adaptation engine automatically triggers the knowledge storage process after passing two-level verification. The process begins by analyzing the event's feature fingerprint. If a record with a similarity exceeding a threshold exists in the feature library, the association weight between that node and the current strategy is strengthened. If the event is a new type, a separate feature node is created and associated with the adaptation strategy parameter package. The graph neural network processor periodically scans all policy deployment effect indicators in the knowledge base, and initiates failure detection for policy nodes with increased false alarm rates or response delays: it traces the changing trends of historical event characteristics associated with the policy, and automatically decouples outdated mapping relationships when it detects that the core characteristics deviate from the original pattern; at the same time, based on the common characteristics of new verification cases, it autonomously generates cross-regional policy migration rules. For example, when the "groundwater level drop" events in arid and semi-arid areas are subject to the same type of water-saving industry regulation parameters, a second-order correlation edge is established between regional type and policy effectiveness.

[0028] Before executing step 4), start the two-level authentication mechanism: First-level verification: The primary warning signal output by the base model and the final warning signal output by the complete model are reversely mapped to the preset environmental carrying capacity constraint space for compliance comparison; Second-level verification: Inject synthetic data streams with preset abnormal patterns into the updated model to monitor changes in early warning response delays and the number of false alarms.

[0029] It is important to note that, during implementation, a two-stage validation process was initiated before deploying the regional adaptation strategy. The first stage of validation involved a compliance check based on ecological principles. The primary warning signal output by the base model and the unverified warning signal generated by the full model were simultaneously projected onto an environmental carrying capacity constraint space, which was pre-configured with rigid rules such as a set of inequalities for sustainable use of water and soil resources and a chain of biodiversity conservation thresholds. Regional adaptation was deemed effective only if the unverified signal satisfied all constraints and the primary signal violated key constraints. For example, if the base model's water consumption warning value for a newly built petrochemical park exceeded the "30% annual runoff" redline, while the adapted warning value fell back within the redline, the validation was successful. The second stage of validation involved engineering robustness stress testing of the updated model. A synthetic data generator constructed a data stream containing pre-defined anomaly patterns, including spikes in pollutant concentrations simulating sudden pollution incidents and adversarial examples denoting broken ecological correlations. The model's response within a pre-defined time window was monitored after the injections. If all pre-defined anomalies were captured and the number of false alarms from conventional monitoring did not increase, the update was deemed reliable. There is a strict progressive relationship between the two-level verification: the second level can be started only after the first level verification is passed. Failure in any link will trigger the adaptation policy rollback and generate a diagnostic report.

[0030] 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 space projection test, the primary warning signal generated by the base model and the regionally adapted signal to be verified are synchronously mapped to a pre-set multidimensional rule matrix. This matrix integrates three core constraint systems: the water and soil resource sustainability inequality, the biodiversity protection threshold chain, and the pollution load capacity boundary.

[0031] When performing compliance comparison, first verify whether the signal to be verified meets all constraint items. If there is any violation, it will be directly judged as invalid. For primary warning signals, the validity judgment is triggered only when it breaks through the key rigid constraints. For example, when the base model output shows that the groundwater extraction in a certain basin exceeds 40% of the annual average recharge, and the adapted result falls back to the 30% safety threshold, the regional adaptation is judged to be valid.

[0032] Implement constraint priority weighting in special scenarios: strengthen the biodiversity constraint weight in ecologically fragile areas, and focus on pollution load boundary verification in industrial agglomeration areas; when the primary signal violates the constraint but the signal to be verified is in a critical state, initiate constraint traceability analysis, locate the core ecological factors that cause the difference and verify their regional correlation, and approve it after confirmation.

[0033] A dynamic early warning platform for regional ecological carrying capacity based on machine learning, including: Data access unit, used to collect ecological data in real time; Feature decoupling processing unit, electrically connected to the data access unit, with a built-in dual-channel deep decoupling network; Dynamic adaptation engine, electrical connection feature decoupling processing unit, including meta-learning controller and lightweight regional adapter; A verification execution unit, electrically connected to the dynamic adaptation engine, for running a two-stage verification mechanism; The early warning decision unit electrically connects the feature decoupling processing unit and the dynamic adaptation engine to perform signal fusion output.

[0034] It should be further explained that during implementation, the platform collects real-time ecological data through the multi-source heterogeneous interfaces of the data access unit. This includes signals from physical devices such as temperature and humidity sensors at weather stations, COD probes in water quality monitors, and satellite remote sensing spectrometers. After pre-processing at the edge computing node, these signals are 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 1 uses an adversarial training chipset to extract universal cross-regional features, while Channel 2 uses a mutual information constraint accelerator card to separate region-specific features.

[0035] The dynamic adaptation engine is connected 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 identified, it triggers the FPGA programmable logic unit to perform incremental fine-tuning on the specified neuron subset of the lightweight regional adapter. If an emergency event is detected, the knowledge base interface is called to inject pre-stored strategies.

[0036] A bidirectional 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.

[0037] 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 operations in the fusion processor, and finally outputs red / orange / yellow dynamic warning signals through the multi-level early warning interface.

[0038] The dynamic adaptation engine is further connected to a meta-knowledge autonomous unit, which includes: Event-strategy mapping database, which stores the association between adaptation strategies and event characteristics; Graph neural network processor, optimizing association relationships in real time.

[0039] It is important to further explain that, during implementation, the meta-knowledge autonomous unit uses an event feature extractor to parse input data streams in real time, constructing a three-dimensional event feature fingerprint. This includes capturing keyword frequency mutation rates from policy text streams, extracting morphological parameters of abnormal regions from remote sensing data streams, and calculating gradient mutation points of ecological indicators from sensor networks. The event-strategy mapping database utilizes a distributed graph storage architecture, with each policy node associated with a four-dimensional effect evaluation vector: warning response delay, false alarm suppression rate, regional adaptability, and knowledge reuse frequency.

[0040] The graph neural network processor periodically performs knowledge topology optimization. When it detects a continuous decrease in the false alarm suppression rate of a policy node or a decrease in fitness below a critical value, it initiates policy failure tracing, analyzes the drift trajectory of the associated event signature fingerprint, 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, signature fingerprint coprocessors and graph computing accelerators are deployed to ensure rapid knowledge iteration. When knowledge conflicts arise, security isolation is initiated: conflicting policies are transferred to a sandbox environment for verification, retaining only the version with the highest overall effectiveness evaluation score.

[0041] The verification execution unit includes a synthetic data generator and an anomaly detector: The synthetic data generator constructs a data stream carrying a preset abnormal pattern; The anomaly monitor records the time it takes for the model to capture injected anomalies and the number of false warnings.

[0042] It is important to further clarify that, during implementation, the synthetic data generator within the verification execution unit incorporates a 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 a toxic substance leak are generated: BTEX concentration spikes are superimposed on a normal water quality monitoring substrate. The spike morphology closely replicates the diffusion curve characteristics of historical accidents, while maintaining chemical equilibrium constraints for associated parameters such as pH.

[0043] Ecological protection zones inject data on food chain disruptions: maintaining normal growth in the vegetation cover index while simultaneously configuring an abnormal decline in the number of migrating birds, creating an ecological contradiction. After data injection, the anomaly monitor initiates multi-dimensional tracking: recording the capture delay of the preset anomaly pattern (i.e., the time difference between injection and the triggering of a red alert), counting the increase in false alarms during the regular monitoring cycle (i.e., comparing the number of false alarms within three days before and after the update), and analyzing in real time whether the model's decision path points to the preset pollution source location area or key factors in species extinction. When the capture delay exceeds the historical fluctuation range, a fault tracing report is automatically generated: noting the unresponsive anomaly pattern feature points, the time period of false alarm growth, and the decision path deviation, triggering a policy rollback mechanism and sending a pulse alarm to the operation and maintenance terminal via an optical signal alarm.

[0044] Platform deployment decoupling - adapting to parallel processing architecture: The base model processing channel and the regional adapter processing channel are independent of each other; The early warning decision unit receives the dual-channel output and performs fusion operations.

[0045] It should be further explained that, during the specific implementation process, the platform adopts a physically isolated dual-channel processing architecture to achieve parallel decoupling and adaptation. The base model processing channel is equipped with a dedicated computing card to continuously run regional invariance feature analysis and output a unified ecological pressure index for the entire domain; the regional adapter processing channel dynamically processes specific features through programmable logic units to generate compensation factors for the target area.

[0046] At the hardware level, the two channels synchronously transmit results to the early warning decision-making unit via independent data buses. When a sudden pollution incident occurs in an industrial zone, the base model channel outputs a "toxic substance cross-regional diffusion risk index," while the adapter channel simultaneously generates a "local river self-purification capacity correction coefficient." The decision-making unit then performs a saturation fusion operation, applying preset threshold constraints to the compensation factor to prevent overcorrection caused by abnormal river characteristic data. When an ecological protection zone experiences drought, the base model channel outputs a "general vegetation water stress model," while the adapter channel calculates a "drought tolerance coefficient for endemic species." Dynamic weighting is activated during fusion, automatically increasing the drought tolerance coefficient weight to 0.8 when monitoring the movement of endangered species. Channel self-tests are initiated in fault scenarios: if the adapter channel outputs a sudden change exceeding three standard deviations of historical fluctuations, the system automatically switches to standalone base model operation and triggers an optical coding alarm to indicate a local failure.

[0047] It is important to note that during implementation, regional ecological data is collected in real time via a multi-source sensor network, including physical signals such as temperature and humidity readings from weather stations, dissolved oxygen concentrations from water quality monitors, and satellite remote sensing vegetation indices. After data ingestion, the system first enters the feature decoupling stage, employing a two-channel deep neural network architecture to separate regional commonalities from specific characteristics. The commonality channel uses an adversarial training mechanism to remove geographically sensitive information. Specifically, a parallel discriminator is set up to identify geographic features. During backpropagation in the main network, the weight of these features is continuously weakened, ultimately outputting cross-regional universals such as water resource metabolism efficiency and pollution diffusion rate. The specificity channel is constrained to minimize mutual information, calculating the statistical correlation between the channel's output features and the commonality features in real time. Gradient descent is used to reduce the correlation strength, ensuring the clean extraction of regionally unique factors such as local water and soil resource parameters and the pollution coefficient of characteristic industries. The outputs of the two channels are then tested for orthogonality by a module. If the feature vector similarity exceeds a set threshold, intensive training is triggered until the target is met.

[0048] While common features are fed into the pre-trained base model to generate primary warning signals, the meta-learning controller continuously scans the target region's data stream. When a slow change in an indicator like the reuse rate of industrial water is detected, it is identified as gradual drift and neuron-level fine-tuning is initiated: the subset of functional neurons in the regional adapter associated with the drift indicator is precisely located, and only small parameter updates are performed on this subset, freezing the rest. If a sudden policy adjustment or ecological disaster signal is identified, the meta-knowledge base is invoked to match historical event strategies: an adaptation parameter package is retrieved based on the similarity of event feature fingerprints, and parameter boundaries are calibrated using short-term data before and after the current event before injection.

[0049] All adaptation strategies must pass a two-level validation process before deployment. The first level maps the primary signals of the base model and the signals to be verified from the complete model onto rigid ecological rule spaces, such as water and soil resource sustainability inequalities and biodiversity chain thresholds, for compliance comparison. This validation is performed only when the signals to be verified fully satisfy the constraints and the primary signals violate key rules. The second level injects synthetic data streams simulating pollution incidents into the system, monitoring the model's response time to pre-set anomaly patterns and fluctuations in the number of regular false alarms. Only when both criteria are met can a new strategy be activated.

[0050] 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, then performs a fusion operation. In the event of sudden pollution in an industrial zone, an upper limit constraint is imposed on the river self-purification correction coefficient to prevent over-correction. In the event of a drought in an ecological protection zone, the drought tolerance coefficient weight is automatically increased when the activity trajectory of endangered species is monitored.

[0051] The platform utilizes a physically isolated dual-channel architecture to ensure real-time performance: the base model processing channel is continuously operated using a dedicated computing card, while the adapter channel is dynamically reconfigured using programmable logic units. 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 independent base model operation mode, and an optically coded alarm is issued.

[0052] The meta-knowledge base autonomously constructs strategy mapping relationships through an event feature extractor. The three-dimensional feature fingerprint is formed by the frequency mutation rate of industrial policy keywords in the policy text, the curvature change of the contaminated area contour in remote sensing images, and the gradient mutation points of sensor network indicators.

[0053] Each verified policy update triggers knowledge base optimization: For policy nodes with declining effectiveness, the drift of associated event features is traced, automatically decoupling failure mappings. Policy migration rules are generated based on cross-regional case commonalities. A graph neural network processor periodically scans the knowledge topology, and a hardware-level accelerator ensures rapid iteration. When knowledge conflicts occur, sandbox verification is initiated, injecting historical data streams to test the overall effectiveness of the policy, and retaining the best and most effective version.

[0054] The synthetic data generator includes built-in scenario-based testing templates. Industrial zone testing utilizes toxic substance concentration pulse superposition technology to rigorously replicate the diffusion curve of a leak accident. Ecological protection zones construct a logical contradiction, demonstrating normal vegetation index growth but abnormal species decline. During stress testing, anomaly monitors track three core metrics: the preset anomaly capture delay must be shorter than a specific multiple of the standard response period; the number of regular false alarms must not fluctuate beyond historical norms; and key nodes in the decision-making process must fully address pollution source location or core factors affecting species extinction.

[0055] 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 missing decision nodes, and the advanced level infers the omissions in factor extraction of the feature decoupling module, providing an accurate basis for rapid repair.

[0056] 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: Step S1: Real-time data streams such as temperature and humidity, pollutant concentration, and vegetation cover index of the target area are collected through meteorological sensors, water quality monitors, and satellite remote sensing equipment, and then input into the feature decoupling network after pre-processing by edge nodes; Step S2: A dual-channel deep network is used to separate features: Channel 1 performs adversarial training to strip geographic identifiers from the data and output universal cross-regional ecological pressure transmission patterns; Channel 2 imposes a mutual information minimization constraint to suppress the statistical correlation between its output and Channel 1, purely extracting regionally unique factors such as local water and soil resource parameters and characteristic industry pollution coefficients; an orthogonal check module verifies the correlation between the output feature vectors of the two channels, triggering enhanced training when the threshold is exceeded; Step S3: Input the universal features output by channel 1 into the pre-trained base model to generate primary warning signals such as the unified water resource shortage index and pollution diffusion risk value for the entire region; Step S4: Event-driven dynamic parameter configuration: When the meta-learning controller detects slow changes in indicators such as industrial water reuse rates, it performs a small incremental update on a subset of associated neurons in the localization region adapter. If a policy keyword mutation or remote sensing anomaly hotspot is identified, a historical event policy parameter package is matched from the meta-knowledge base and injected into the adapter after calibration with current short-term data. The adapter then outputs regional compensation signals such as the self-purification correction coefficient for industrial zone rivers and the drought tolerance factor for species in protected areas. Step S5: A progressive verification strategy, including: First-level ecological compliance testing: mapping the primary signal of the base model and the signal to be verified to a rule space such as the water and soil resource inequality group and the biodiversity chain threshold. The verification passes only when the signal to be verified is fully compliant and the primary signal violates the key constraints; Second-level engineering robustness testing: injecting a synthetic data stream simulating a chemical leak into the system, monitoring the preset anomaly capture delay and the fluctuation of the number of regular false alarms, and activating the new strategy when both standards are met; Step S6: The ecological pressure index and regional compensation factor output by the base model are input into the fusion processor in parallel. The industrial zone scenario imposes an upper limit constraint on the pollution correction coefficient to prevent over-correction caused by data anomalies. When the ecological protection zone monitors the trajectory of endangered species, the drought tolerance factor is automatically weighted to a dominant position. Step S7: Extract characteristic fingerprints such as the policy keyword frequency mutation rate and the curvature change of the contaminated area contour of the new event; when the false alarm suppression rate of the policy node continuously decreases, trace the drift trajectory of the associated event characteristics and decouple the failure mapping; generate policy migration rules based on the commonality of cross-regional cases and strengthen the connection weight of the target area node; Step S8: The base model channel is run by a dedicated computing card, and the adapter channel is dynamically reconstructed through the programmable unit; the fluctuation range of the channel output value is monitored in real time, and when the historical threshold is exceeded, the abnormal channel is frozen and switched to the base model independent operation mode; the pulse light coding alarm is triggered and a fault location report is generated.

[0057] Through the dual-channel feature decoupling mechanism, the risk of model failure during cross-regional deployment is eliminated, and the early warning system automatically adapts to the characteristics of different geographical environments; the dynamic meta-adaptation architecture achieves rapid response and quickly generates accurate regional compensation strategies in sudden environmental events, which improves efficiency compared to full model retraining; the dual closed-loop verification system simultaneously ensures compliance with ecological principles and engineering robustness, eliminating the problem of early warning results violating scientific laws or delayed responses from the root.

[0058] The platform-level decoupling-adaptive parallel architecture breaks through the serial processing bottleneck, enabling simultaneous completion of ecological pressure analysis and regional compensation calculations, and improving the timeliness of early warnings for major pollution accidents; the hardware fault isolation mechanism ensures the maintenance of basic early warning functions under extreme working 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 regions, and promoting ecological governance from passive response to active protection.

[0059] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0060] 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 these 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.

Claims

1. A dynamic early warning method and platform for regional ecological carrying capacity based on machine learning, characterized by: The steps include: 1) A dual-channel deep decoupling network is used to separate region-invariant features from region-specific features in the input data; 2) Inputting regional invariance features into the base model to generate primary warning signals; 3) Using a meta-learning controller to monitor data drift indicators and emergency event signals in the target area, dynamically generating configuration parameters for the lightweight regional adapter; 4) Using the configured lightweight regional adapter to process region-specific features and output a region-adaptive compensation signal; 5) The primary warning signal is integrated with the regional adaptive compensation signal to generate the final warning result.

2. The method and platform for dynamic early warning of regional ecological carrying capacity based on machine learning according to claim 1, characterized in that: In step 1), the dual-channel deep decoupling network achieves feature orthogonal separation in the following ways: The regional invariance feature channel uses adversarial training constraints to output universal ecological carrying capacity laws across regions; The region-specific feature channel outputs localized environmental sensitive factors through mutual information minimization constraints.

3. The method and platform for dynamic early warning of regional ecological carrying capacity based on machine learning according to claim 1, characterized in that: The lightweight regional adapter in step 3) is a super network architecture, and its parameter configuration method includes: Enable neuron-level incremental fine-tuning for gradual data drift; For sudden events, the adaptation strategy injection parameters of the matching scenario in the meta-knowledge base are called.

4. The method and platform for dynamic early warning of regional ecological carrying capacity based on machine learning according to claim 3, 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; Graph neural networks are used to associate new verification cases to update the strategy mapping logic.

5. The method and platform for dynamic early warning of regional ecological carrying capacity based on machine learning according to claim 1, characterized in that: Before executing step 4), start the two-level authentication mechanism: First-level verification: The primary warning signal output by the base model and the final warning signal output by the complete model are reversely mapped to the preset environmental carrying capacity constraint space for compliance comparison; Second-level verification: Inject synthetic data streams with preset abnormal patterns into the updated model to monitor changes in early warning response delays and the number of false alarms.

6. The method and platform for dynamic early warning of regional ecological carrying capacity based on machine learning according to claim 5, characterized in that: The compliance comparison of the first-level verification is: 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 judged to be valid.

7. A regional ecological carrying capacity dynamic early warning platform based on machine learning, characterized by: include: Data access unit, used to collect ecological data in real time; Feature decoupling processing unit, electrically connected to the data access unit, with a built-in dual-channel deep decoupling network; Dynamic adaptation engine, electrical connection feature decoupling processing unit, including meta-learning controller and lightweight regional adapter; A verification execution unit, electrically connected to the dynamic adaptation engine, for running a two-stage verification mechanism; The early warning decision unit electrically connects the feature decoupling processing unit and the dynamic adaptation engine to perform signal fusion output.

8. The regional ecological carrying capacity dynamic early warning platform based on machine learning according to claim 7 is characterized by: The dynamic adaptation engine is further connected to a meta-knowledge autonomous unit, which includes: Event-strategy mapping database, which stores the association between adaptation strategies and event characteristics; Graph neural network processor, optimizing association relationships in real time.

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

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

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